WO2002095650A2

WO2002095650A2 - Method for determination of co-occurences of attributes

Info

Publication number: WO2002095650A2
Application number: PCT/CA2002/000731
Authority: WO
Inventors: Max Kotlyar; Roland Somogyi; James Green; Evan Steeg; Alan D. Ableson
Original assignee: Molecular Mining Corporation
Priority date: 2001-05-21
Filing date: 2002-05-17
Publication date: 2002-11-28
Also published as: CA2447857A1; US20040158581A1; AU2002302243A1; WO2002095650A3

Abstract

A method, system, computer program selecting attribute sets of characterizing attributes of an object, selecting an attribute set of attributes of interest, assigning a likelihood for each characterized attribute set that the attribute set occurs when the attribute set of interest occurs (each likelihood determined using Bayesian computable classifiers on a dataset of attributes for actual samples), comparing each assigned likelihood against likelihood thresholds, and reporting the assigned likelihoods of the characterizing attribute set based on the likelihood thresholds. Markers may be identified for diagnosis and prognosis. Characterizing attributes may be gene expression levels and the attribute of interest may be drug sensitivity level, drug dose (absolute concentration or dose relative to some standard dose), dose of drug which causes half-maximal cellular growth rate, or logarithm base 10 (dose) where dose is the dose which yields half-maximal total cell mass accumulating.

Description

Determination of Co-Occurrences of Attributes

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority from United States patent application serial no. 60/291,928 filed May 21, 2001 by the same inventors under the same title, and from United States patent application serial no. 60/291,931 filed May 21, 2001 by the same inventors under the title Methods of Gene Analysis and Treating Cancer. United States patent application serial nos. 60/291,928 and 60/291,931 are hereby incorporated herein by reference. TECHNICAL FIELD

The invention relates to methods and apparatuses for determining co-occurences of attributes in objects. It also relates to attributes including biological response.

BACKGROUND ART

The discovery of correlations among pairs or k-tuples of variables has applications in many areas of science, medicine, industry and commerce. For example, it is of great interest to physicians and public health professionals to know which lifestyle, dietary, and environmental factors correlate wito each other and with particular diseases in a database of patient histories. It is potentially profitable for a trader in stocks or commodities to discover a set of financial instruments whose prices covary over time. Sales staff in a supermarket chain or mail-order distributor would be interested in knowing that consumers who buy product A also tend to buy products B and Q and this can be discovered in a database of sales records. Computational molecular biologists and drug discovery researchers would like to infer aspects of molecular structure from correlations between distant sequence elements in aligned sets of RNA or protein sequences.

One formulation ofthe general problem which encompasses many diverse applications, and which facilitates understanding ofthe principles described herein is a matrix of discrete features in which rows correspond to "objects" (such as diseases, individual patients, stock prices, consumers, or protein sequences) and the columns correspond to features, or attributes, or variables (such as drug sensitivity, gene expression, lifestyle factors, stocks, sales items, or amino acid residue positions). Given the vast amount of data and the valuable nature ofthe information available from large datasets, one wants to use efficient techniques to assist in the determination of correlations. For example, large-scale datasets exists of DNA microarray studies. These can be used to determine correlations between gene expression patterns and drug treatments. This approach is urgently needed for the treatment of many diseases and other conditions, for example cancer which involves many different tissues and varieties of tumor types. However, the application ofthe proper data analysis methods will be critical for the efficient use of these large-scale data sets.

Biologists are generally acquainted with the idea of correlating individual genes with specific physiological functions, and with the use of linear correlation methods, such as Pearson's correlation coefficient. Although the linear, single-gene approach has yielded significant advances in biomedicine, the complex, nonlinear nature of tissue demands the use of more sophisticated methods.

It is desirable to provide efficient means by which to determine correlations between attributes of objects.

DISCLOSURE OF THE INVENTION

In a first aspect ofthe invention provides, a base method for identifying one or more characterizing attributes for an object that are likely to co-occur with one or more attributes of interest for the object. The method comprises the steps of selecting one or more attribute sets of one or more characterizing attributes ofthe object, selecting an attribute set of one or more attributes of interest for the object, assigning a likelihood for each characterized attribute set that the attribute set occurs for the object when the attribute set of interest occurs for the object (each likelihood determined using one or more Bayesian computable classifiers on a dataset of attributes for a plurality of actual samples ofthe object), comparing each assigned likelihood against one or more likelihood thresholds, and reporting the assigned likelihoods ofthe characterizing attribute set based on the likelihood thresholds.

In another aspect the invention provides, a method comprising the steps of, selecting one characterizing attribute set of one or more attributes for the object, selecting an attribute of interest for the object, assigning a likelihood for the characterized attribute set that the attribute occurs for the object when the attribute of interest occurs for the object (the assigned likelihood determined using a Bayesian computable classifier on a dataset of attributes for a plurality of actual samples ofthe object), comparing the assigned likelihood against a likelihood threshold, and reporting the assigned likelihood ofthe characterizing attribute set based on the likelihood threshold.

In another aspect the invention provides, a method comprising the steps of, selecting one or more attribute sets of one or more characterizing attributes ofthe object, selecting an attribute set of one or more attributes of interest for the object, assigning a likelihood for each characterized attribute set that the attribute set occurs for the object when the attribute set of interest occurs for the object (each likelihood determined using one or more Bayesian computable classifiers on a dataset of attributes for a plurality of actual samples ofthe object), determining a likelihood significance for each assigned likelihood using artificial samples, and ranking the assigned likelihoods ofthe characterizing attribute set using the likelihood significance.

In another aspect the invention provides, a method comprising the steps of accessing one ofthe systems described below. In another aspect the invention provides, a base system used to identify one or more characterizing attributes for an object that are likely to co-occur with one or more attributes of interest for the object using a dataset of samples of attributes for the object. The system comprises a computing platform, and a computer program on a computer readable medium for use on the computer platform in association with the dataset. The computer program comprises instructions to identify a characterizing attribute for an object that is likely to co-occur with an attribute of interest for the object, by carrying out the steps of one ofthe base methods.

The methods may be used for drug discovery by identifying characterizing attribute sets for interaction by the drug using the steps one ofthe base methods for drug sensitive attributes of interest drug, and performing screens for drugs where growth in cells having desirably ranked characterizing attribute sets is drug sensitive.

The methods may be used for identifying markers for diagnostic kits used to determine if a treatment is appropriate for a patient, by identifying a gene expression level set to be tested for in the patient by carrying out the steps of one ofthe base methods. The methods may be used for identifying markers for diagnosis of a living system by identifying an attribute set to be tested for in the living system using the steps of one of the base methods. The methods may also be used for identifying markers for prognosis of a living system by identifying an attribute set to be tested for in the living system using the steps of one ofthe base methods. The diagnosis or prognosis may be with . respect to a disease or syndrome type of a patient. The methods may also be used for identifying markers for determing the appropriateness of a therapy or treatment of a living system by identifying an attribute set to be tested for in the living system using the steps of one ofthe base methods.

In the above methods the attributes ofthe attribute set may include protein concentrations. The protein concentrations may include tissue protein concentrations. The protein concentrations may include serum protein concentrations.

In the above methods the attributes ofthe attribute set may include molecular markers. The molecular markers may include blood molecular markers. The molecular markers may include tissue molecular markers.

In the above methods the attributes ofthe attribute set may include clinical observables. The clinical observables may include microscopic clinical observables. The clinical observables may include macroscopic clinical observables.

The markers may be for diagnostic kits used in the diagnosis, for diagnostic procedures used in the diagnosis, for prognostic kits used in the prognosis, or for prognostic procedures used in the prognosis. A likelihood threshold for each characterizing attribute set may be determined using the same Bayesian classifiers as the assigned likelihood on a dataset of attributes for a plurality of artificial samples ofthe object. Similarly, a likelihood threshold for each characterizing attribute set may be determined by computing those characterizing attribute sets with an assigned likelihood above a given percentile of all assigned likelihoods for the relevant attribute set.

Artificial samples may be created by randomizing the actual gene expression levels for the characterizing attributes. Artificial samples may be created by transposing the actual gene expression levels for each characterizing attribute to another characterizing attribute. The assigned likelihoods ofthe characterizing attribute sets may be compared against a likelihood threshold determined by computing those characterizing attribute sets with an assigned likelihood above a given percentile of all assigned likelihoods for the relevant attribute set of interest.

The characterizing attributes may be gene expression levels and the attribute of interest may be drug sensitivity level, drug dose (absolute concentration or dose relative to some standard dose) along an increasing or decreasing scale, dose of drug which causes half- maximal cellular growth rate, or -logarithm₁₀(dose) where dose is the dose which yields half-maximal total cell mass accumulating under otherwise standard conditions.

Drug sensitivity level may represent growth inhibiting in diseased cells, a lack of growth inhibiting in diseased cells, patient toxicity in healthy cells. The attributes may be represented in a dataset taken from the NCI60 dataset. The Bayesian classifier may be selected from a group consisting of linear discriminant analysis, quadratic discriminant analysis, and a uniform gaussian analysis.

The characterizing attribute sets ranked following comparison ofthe likelihood and the likelihood threshold may be reported. The ranked characterizing attributes sets may be reported to one of a group consisting of a computer readable file stored on computer readable media, a printed report, and a computer network. The assigned likelihoods may be ranked by assigned likelihood and subranked by likelihood significance. The assigned likelihood may be compared against a likelihood threshold, and the assigned likelihood ofthe characterizing attribute set may be reported based on the likelihood threshold and the ranking ofthe assigned likelihood.

BRIEF DESCRIPTION OF THE DRAWINGS

For a better understanding ofthe present invention and to show more clearly how it may be carried into effect, reference will now be made, by way of example, to the accompanying drawings that show the preferred embodiment ofthe present invention and in which:

FIG. 1 is a first Venn diagram of statistically significant results of analyses employed in the preferred embodiment ofthe invention;

FIG. 2 is a second Venn diagram of statistically significant results of analyses employed in the preferred embodiment of the invention; FIG. 3 is a plot of results from a 2D QDA analysis of a dataset according to the preferred embodiment ofthe invention;

FIG. 4 is a plot of results from a 2D LDA analysis of a dataset according to the preferred embodiment ofthe invention; FIG. 5 is a plot of results from a 2D QDA analysis of a dataset according to the preferred embodiment ofthe invention;

FIG. 6 is a plot of results from a 2D UGDA analysis of a dataset according to the preferred embodiment ofthe invention;

FIG. 7 is a plot of results from a ID LDA analysis of a dataset according to the preferred embodiment of the invention;

FIG. 8 is a plot of results from a ID UGDA analysis of a dataset according to the preferred embodiment ofthe invention;

FIG. 9 is an example flow chart of a computer program according to the preferred embodiment ofthe invention; FIG. 10 is an example block diagram of a system according to the preferred embodiment ofthe invention;

FIG. 11 is an example flow chart of a computer program according to an alternate embodiment ofthe invention;

FIG. 12 is an example block diagram of a system according to an alternate embodiment ofthe invention;

FIG. 13 is an example flow chart of a computer program according to an alternate embodiment ofthe invention;

FIG. 14 is an example block diagram of a system according to an alternate embodiment ofthe invention; FIG. 15 is an example flow chart of a computer program according to an alternate embodiment ofthe invention; and

FIG. 16 is an example block diagram of a system according to an alternate embodiment ofthe invention. MODES FOR CARRYINGOUTTHE INVENTION

A number of alternative base methods, systems and devices will now be referred described, along with alternative applications for those methods, systems and devices. It is understood that these base methods, systems and devices and their alternative applications are by way of description of preferred embodiments and are not limiting to the principles described and the application of those principles.

As previously set out, a base method identifies one or more characterizing attributes for an object that are likely to co-occur with one or more attributes of interest for the object. The method comprises the steps of selecting one or more attribute sets of one or more characterizing attributes ofthe object, selecting an attribute set of one or more attributes of interest for the object, assigning a likelihood for each characterized attribute set that the attribute set occurs for the object when the attribute set of interest occurs for the object (each likelihood determined using one or more Bayesian computable classifiers on a dataset of attributes for a plurality of actual samples ofthe object), comparing each assigned likelihood against one or more likelihood thresholds, and reporting the assigned likelihoods ofthe characterizing attribute set based on the likelihood thresholds.

In an alternative base method, the method comprises the steps of, selecting one characterizing attribute set of one or more attributes for the object, selecting an attribute of interest for the object, assigning a likelihood for the characterized attribute set that the attribute occurs for the object when the attribute of interest occurs for the object (the assigned likelihood determined using a Bayesian computable classifier on a dataset of attributes for a plurality of actual samples ofthe object), comparing the assigned likelihood against a likelihood threshold, and

Reporting the assigned likelihood ofthe characterizing attribute set based on the likelihood threshold.

In a further alternative base method, the method comprises the steps of, selecting one or more attribute sets of one or more characterizing attributes ofthe object, selecting an attribute set of one or more attributes of interest for the object, assigning a likelihood for each characterized attribute set that the attribute set occurs for the object when the attribute set of interest occurs for the object (each likelihood determined using one or more Bayesian computable classifiers on a dataset of attributes for a plurality of actual samples ofthe object), determining a likelihood significance for each assigned likelihood using artificial samples, and ranking the assigned likelihoods ofthe characterizing attribute set using the likelihood significance.

In a further alternative base method, the method comprises the steps of accessing one of the systems described below. As previously set out a base system is used to identify one or more characterizing attributes for an object that are likely to co-occur with one or more attributes of interest for the object using a dataset of samples of attributes for the object. The system comprises a computing platform, and a computer program on a computer readable medium for use on the computer platform in association with the dataset. The computer program comprises instructions to identify a characterizing attribute for an object that is likely to co-occur with an attribute of interest for the object, by carrying out the steps of one ofthe base methods.

The base methods can be used for drug discovery by identifying characterizing attribute sets for interaction by the drug using the steps one ofthe base methods for drug sensitive attributes of interest drug, and performing screens for drugs where growth in cells having desirably ranked characterizing attribute sets is drug sensitive.

The base methods can be used for identifying markers for diagnostic kits used to determine if a treatment is appropriate for a patient, by identifying a gene expression level set to be tested for in the patient by carrying out the steps of one ofthe base methods.

In the base methods, a likelihood threshold for each characterizing attribute set can be determined using the same Bayesian classifiers as the assigned likelihood on a dataset of attributes for a plurality of artificial samples ofthe object. Similarly, a likelihood threshold for each characterizing attribute set can be determined by computing those characterizing attribute sets with an assigned likelihood above a given percentile of all assigned likelihoods for the relevant attribute set.

Artificial samples can be created by randomizing the actual gene expression levels for the characterizing attributes. Artificial samples can be created by transposing the actual gene expression levels for each characterizing attribute to another characterizing attribute. The assigned likelihoods ofthe characterizing attribute sets may be compared against a likelihood threshold determined by computing those characterizing attribute sets with an assigned likelihood above a given percentile of all assigned likelihoods for the relevant attribute set of interest. For the base methods, the characterizing attributes may be gene expression levels and the attribute of interest may be drug sensitivity level, drug dose (absolute concentration or dose relative to some standard dose) along an increasing or decreasing scale, dose of drug which causes half-maximal cellular growth rate, or - logarithm ₁₀ (dose) where dose is the dose which yields half-maximal total cell mass accumulating under otherwise standard conditions.

Drug sensitivity level may represent growth inhibiting in diseased cells, a lack of growth inhibiting in diseased cells, patient toxicity in healthy cells. The attributes may be represented in a dataset taken from the NCI60 dataset. The Bayesian classifier may be selected from a group consisting of linear discriminant analysis, quadratic discriminant analysis, and a uniform/gaussian analysis.

The modes described herein provide extensions and alternatives to the base methods described above and employ many similar principles. The principles of one application as described herein may be applied to the others as appropriate. Thus, the description of all elements of each application will not always be repeated for all applications.

In the preferred embodiment it is preferred for simplicity of programming and interpretation to consider the object and attributes in the form of a matrix, see for example Table 1; however, this is not strictly required and any ofthe embodiments can utilize a data set of objects and attributes that are not represented in the form of a matrix by sampling the data set directly.

Table 1

As an example of a dataset laid out in matrix format, the objects may be a particular disease, while the samples are taken from different patients and the attributes are particular expression levels of particular genes and sensitivity to a particular drug. The samples may be cells. Using the data in Table 1, sample 1 from a cell having disease A is taken from a first patient. The disease A cell from the patient has sensitivity to drug I and gene expression levels d, e, f. Similarly, sample 2 from a cell having disease B may also be taken from the same patient. The disease B cell from the patient has sensitivity to drug II and gene expression levels d, g, h. Sample 3 from a cell having disease A is taken from a different patient. The disease A cell from the patient has sensitivity to drug I and gene expression levels d, h. For the example set out above, we may be interested in whether or not sensitivity to drug I is related somehow to gene expressions levels d and e together. Thus, drag I is an attribute set of interest and gene expression levels d and e are a characterizing attribute set. This may be represented in a matrix in the form of Table 2.

Table 2

Alternatively, object A and object B may be part of a generic object C. For example, one may be interested in knowing if a number of forms of cancer are sensitive to the same drag. In this case, the relevant samples may change. In the example above, the first patient has two forms of cancer A and B. If one is looking for drag sensitivity in both cancers A and B then the all the samples may be relevant, while the object is cancers of type A and B. This permits the use of samples from the same patient for different cancers. Samples from the same patient with the same attribute of interest would ordinarily be considered to be only one sample. The particular definition of objects, samples, attributes of interest and characterizing attributes is a matter of choice for the designer of a particular embodiment. It is recognized that some choices may be superior to others; however, that does not bring them any of them outside ofthe principles described herein.

The datasets may contain many different samples, some of which will not contain attribute sets of interest for a given run ofthe methods. These can be filtered out before the methods are run, or they may be left in the dataset to be accessed when the methods are ran.

Each ofthe features for an object may be numerical or qualitative. The features are transformed into ordinal (values capable of being ordered) variables, termed attributes. The principles described herein can be extended to attributes sets of interest and characterizing sets of higher orders. For example, one may want to know if sensitivity to a particular cocktail of drags co-occurs with a particular combination of gene expression levels.

In this description, specific reference is made on many occasions to examples in the biotech industry. This is in no way limiting to the broad nature ofthe principles described herein which may be applied to many industry including, by way of example only, financial services, drug discovery, discovery and analysis of genetic networks, sales analysis, direct mail and related marketing activities, clustering customer data, analysis of medical, epidemiological and public health databases, patient data, causes of failures and the analysis of complex systems. When using the phrases "occurs for" and "attributes for" in respect of an object, it is understood that these are broadly intended. Attributes may not simply be a part of an object, such as its gene expression levels, but may be factors or things that could broadly be related to the object, such as weather on a particular day (attribute) may be related to the price (attribute) of an agricultural stock (object). It is also understood that objects are not limited to traditionally tangible objects, but may be intangible objects such as bonds or stocks as well.

It is recognized that a characterizing attribute set that is likely to co-occur with an attribute set of interest does not necessarily imply that the characterizing attribute set is causing the attribute of interest; however, in many situations this information continues to be useful. For example, symptoms (characterizing attributes) may act as a useful disease marker (attribute of interest); however, they are caused by, and do not generally cause, the disease.

The methods can form part of methods for identifying possible drug targets. Once it is known that a disease or diseased cell is affected by drags that appear to interact with cells having particular combinations of gene expression levels then screening studies can be conducted to find other drags that also inhibit growth in cells with those combinations of expression levels.

The base method takes a dataset of samples of objects, including a characterizing attributes set and an attribute set of interest, as input. The method generates an output display of characterizing attribute sets that have a substantial likelihood of co-occurring with the attribute set of interest.

As part ofthe method, one or more characterizing attribute sets are selected, and one or more attribute sets of interest are selected. The likelihood of each characterizing attribute set co-occurring in actual samples ofthe object is determined using a Bayesian computable classifier. A likelihood of each characterizing set occurring in artificial samples is used to determine a likelihood threshold. Only those characterizing attribute sets with a likelihood co-occurrence greater than its likelihood threshold is selected.

For example, an embodiment ofthe method may take a collection of biological samples, their gene expression measurements (characterizing attributes), and a binary high low drug response measurement (attributes of interest) as input. The method generates a prioritized list of genes, ranked by their p- values or ability to correctly predict the drug response (likelihood of co-occurrence). In this example, the method consists of three steps:

1) Selection of candidate gene sets (characterizing attribute set).

2) Calculation of classification accuracy for each gene set using a Bayesian classifier (determination of likelihood of co-occurrence using Bayesian classifier)

3) Ranking ofthe gene sets by their classification accuracy and the identification of meaningful gene sets by a comparison of their classification accuracies with those generated using randomized data (determination of likelihood threshold using artificial samples and selection of characterizing attribute sets having a substantial likelihood of co-occurrence) .

Step 1) can take a number of forms. A simple list of all single genes can be a collection of (singleton) gene sets. A list of all pairs of genes can be a collection of (gene pair) candidate gene sets. Pre-processing techniques (such as those described in PCT Patent Application PCT/CA98/00273 filed March 23 1998 under title Coincidence Detection Method, Products and Apparatus, inventor Evan W. Steeg, published October 1 1998 as WO 98/43182) may be used to create candidate gene sets. Alternative pre-processing techniques may be used, including by way of example, standard feature detectors, or known gene pathway tables.

Step 2) can also take a number of forms. Classical statistical techniques such as Linear Discriminant Analysis or Quadratic Discriminant Analysis can be used. Other probabilistic models, such as the Gaussian/Uniform, can be tailored to particular applications or to suit biological intuition.

Step 3) involves the comparison ofthe classification scores from step 2) to those generated from randomized data. Multiple datasets (on the order of 100 or more) are generated by permuting the gene expression values over the samples, i.e. if samples were rows and genes were columns in a table, we would permute the entries in each column, independently. Steps 1) and 2) are repeated for the randomized data, and the scores from the real data are compared to the scores from the randomized data. The scores are ranked according to those most likely to indicate a cooccurrence and those scores greater than the scores for randomized data. Selections can be made according to the rank ofthe scores for the non-randomized data, or according to the rank ofthe difference ofthe scores for the real and randomized data. Selections may also be based on other calculations using the real and random scores.

By way of example, validation can be determined either by comparing classification scores from the real data to all the classification scores from the randomized data and then applying the Bonferroni correction, or by comparing the most extreme classification accuracies from each randomized trial to the most extreme classification accuracy from the real data. An empirical p-value can be obtained directly by calculating the proportion of random datasets for which their extreme classification accuracies exceeded that in the real data. Only those gene sets with p- values below a user-selected cutoff are reported.

The results ofthe method described above have many uses including, by way of example, to use the:

1) gene sets identified as potential targets for drag interaction.

2) gene sets identified for pre-treatment screening of patients to identify the most effective drug treatment.

We analyzed data on the responses of 60 human cancer cell lines (NCI60) to 90 drags shown to inhibit their growth in culture (Developmental Therapeutics Program, National Cancer Institute). These data were correlated with the basal (untreated) gene expression patterns from the same set of cell lines (see Ross, D. T., Scherf, U., Eisen, M. B., Perou, C. M., Rees, C, et al. (2000) Systematic variation in gene expression patterns in human cancer cell lines. Nature 24, 227-235, and Scherf, U., Ross, D. T., Waltham, W., Smith, L. H., Lee, J. K., et al. (2000) A gene expression database for the molecular pharmacology of cancer. Nature 24, 236-244).

We compared linear and nonlinear methods for correlating gene expression levels of individual genes with drug sensitivity for 1000 genes across the 60 cancer cell lines, which included breast, central nervous system, colon, lung, renal, and prostate cancer, as well as melanoma and leukemia cell lines. In addition, we correlated the expression patterns of pairs of genes with drag sensitivities to determine whether more than one gene was required to predict drag sensitivity in some cases. We found that linear and non-linear methods captured different, although to some extent overlapping, correlations, suggesting specific genes as markers for particular drag treatments. We also found that expression levels of combinations of genes should be considered as indicators of effective drag treatments, as these combinations sometimes contain information not found in the expression patterns of individual genes considered in isolation.

We conclude that nonlinear and combinatorial, as well as linear, single-gene methods are appropriate for the efficient extraction of gene expression-drag sensitivity relationships in cancer cell lines. Computational methods such as these should be useful in cancer diagnosis and treatment.

First, we divided drug sensitivity into low- and high-sensitivity classes (creating possible attributes of interest):

Drag sensitivities were reported as -logGI50 s, with the log being base 10. All the drag sensitivities were normalized to mean zero so that the measurement really reflected differential growth inhibition. We wanted to categorize the cell line response into "uninhibited" and "inhibited", with a small gray area to avoid the effects of harsh cutoffs. In that scale, a value of 1.0 for a cell line/drag combination meant that the cell line was inhibited to 50% growth at 1/10 the dosage ofthe "average" drug. For our purposes, we wanted to identify those drags that were effective at least 1/5 the "average" dosage, which in the log scale turns into 0.7. Thus, any value of-logGI50 less than 0.7 were considered "uninhibited" or a low sensitivity/response. On the other end ofthe scale, all of those drugs that resulted in inhibition at concentrations < 1/10 of the average dosage were all considered "inhibitory". We then put in a smooth linear scaling between the cutoffs of 0.7 (low response) and 1.0 (high response). This gave us the function: f(r) = 0 ifr < 0.7

(r-0.7)/0.3 ifr in [0.7, 1) 1 ifr >= 1

Sensitivities in the range [0.7,1] are partially in both classes. Since it varies between 0 and 1 , the function f can be viewed as a fuzzy classification or a probability. f(r) = Probability of sensitivity in high class, l-f(r) = Probability of sensitivity in low class. Finding correlations (determining likelihood of co-occurrence of attribute set of interest and characterizing attribute set) between drag sensitivity (attribute set of interest) and gene expression (characterizing attribute set):

For a given gene, A, and drug, B, we try to see if 2 classes of cell lines (high and low sensitivity) can be distinguished on the basis of gene expression. One ofthe methods for finding correlations was a slightly modified version of LDA (slightly modified to account for partial class membership). LDA consists ofthe following steps:

Fit a gaussian Gh to the gene expressions in the high sensitivity class Ch and a gaussian Gh to gene expressions in the low sensitivity class CI, where |Ch| is the number of cell lines in the high sensitivity class, and |C1| is the number of cell lines in the low sensitivity class.

Let Lexpr = expression of gene A in cell line L, Lsensitivity= sensitivity of cell line L to drag B

The mean of Gl is calculated as sum from cell line L = 1 to |Ch| of (Lsensitivity * Lexpr) / (sum of sensitivities in Ch)

Mean and variance of Gl were calculated in a similar way.

Pooled variance of Gh and Gl was calculated avg. variance = (Ch variance * sum Ch sensitivities + CI variance * sum CI sensitivities )/ (num cell lines - 2 -1) .We calculated the probability of a cell line, L, having high sensitivity as follows

P(L in Ch | Lexpr) = Gh(Lexpr) * P(Ch) / (Gh(Lexpr)*P(Ch) + (Gl(Lexpr)*P(Cl) ) above is Equation 1

The error for this probability was calculated as e = Lsensitivity - P(L in Ch j Lexpr)

Testing predictions:

For a given gene and drag we used cross-validation to test prediction of sensitivity from gene expression. Using 59 cell lines we determined gaussians Gh and Gl for the two sensitivity classes. We predicted the sensitivity class ofthe 60th cell line L, from its gene expression, using the Equation 1 above. We repeated this procedure for all ofthe 60 cell lines and calculated a mean squared error for all ofthe predictions, e = sum L = 1 to 60 [P(L in Ch | Lexpr) - L sensitivity]^Λ2 / 60.

Searching for all correlations: We applied the above method to all pairs of genes and drags [1000 genes] x [90 drags]

Using other methods:

ID discriminants we also used 2 other methods similar to LDA, to search for correlations between sensitivity and gene expression

QDA - differs from LDA in that the original variances of Gh and Gl are used in Equation 1, instead ofthe average ofthe variances as a result, QDA can have nonlinear decision boundaries between classes while LDA has linear decision boundaries. uniform/gaussian discriminant - similar to LDA except uses uniform distribution for the low class instead of a gaussian distribution, the assumption behind these distributions is that a specific mechanism is responsible for high sensitivity (the gaussian distribution), while various mechanisms lead to low sensitivity (uniform distribution), the height ofthe uniform is calculated as l/(max(expr) - min(expr)) 2D discriminants

The three methods above were extended to look for correlations between pairs of genes and drag sensitivities. For a given pair of genes, the joint distribution of gene expression values was represented by gaussians and uniform distributions. A search for correlations was conducted over all pairs of genes and all drugs. For each drag, the three methods were applied to about 1/2 million (gene,gene,drag) triples.

Calculating statistical significance (a likelihood threshold):

The statistical significance of MSE scores was determined by comparing against results from randomized data. Statistical significance was adjusted by the Bonferroni method to account for multiple tests, (i.e. for a given drag the statistical significance of a score from a ID discriminant was multiplied by 1000; statistical significance of scores from 2D discriminants was multiplied by 10^A5).

To determine whether linear and nonlinear methods could capture different sets of gene expression-drug sensitivity correlations, we employed linear discriminant analysis (LDA) and two nonlinear methods, quadratic discriminant analysis (QDA) and a Bayesian model (a uniform/Gaussian discriminant). Results are shown in Table 3 below.

Table 3

Table 3 summarizes linear, nonlinear, ID, and 2D analyses for 1000 genes, 90 drugs, and 60 cell lines. Shown are the numbers of statistically significant gene-drag associations found at p <= 0.01 and p <= 0.1. For example, the LDA-1D analysis method found that for each of 8 drugs, at least one gene out of a group of 14 was able to predict high sensitivity at p <= 0.01. For LDA-2D, 24 genes arranged in pairs were able to predict high sensitivity to each of 9 drags at p <= 0.01.

All three methods identified statistically significant correlations between the expression levels of specific genes and sensitivity to drags based on GI50 values (drug concentration that inhibits cell growth by 50%). Although there was some overlap between the findings ofthe different methods, they were generally complementary to one another, as shown by the Venn diagrams of statistically significant results from all analysis methods in Figs. 1 and 2. A degree of overlap occurs between results obtained; however, some ofthe gene-drug correlations were identified by a single method. As shown in Fig. 1, twenty-six drags (represented by intersection 1) ofthe 29 drags

(represented by circle 3) found to be in significant correlations with genes by linear ID methods (LDA ID) were also identified by at least one other method in the non-linear and combinatorial methods that identified 52 drugs (represented by circle 5), leaving 3 drags (represented by the non-intersecting portion 7 of circle 3) that were identified by LDA ID alone. Similarly, as shown in Fig. 2, five genes (non-intersecting portion 9) out of 43 (circle 11) that were identified by LDA ID as markers for drag sensitivity were identified by that method alone, while the remaining 38 genes (intersection 13) were identified by at least one ofthe other methods in addition to LDA ID out of a total of 234 genes (circle 15) that were identified by the other methods. Nonlinear methods therefore identify gene-drag associations not found by a linear method. This is the case for both 1 -dimensional (ID) analysis involving correlations between a single gene and one drug, and for 2D analysis involving correlations between pairs of genes and one drug (gene, gene, drug triples).

To discover correlations between gene expression levels and drag sensitivities that involve more than a single gene, (i.e., the information that predicts high sensitivity to a drug may be contained in the combination of expression patterns of two genes), we applied 2D discriminants. This involved using the same three methods described above for single genes, except that in this case we searched for significant correlations between pairs of genes and individual drags, i.e., gene, gene, drug triples. Results for 2D methods are shown in Table 3 and Figs. 1 and 2. The 2D methods discovered correlations that were not identified by the ID method. It is evident from Figs. 1 and 2 and Table 3 that relying only on single-gene (ID) correlations would have missed a large proportion ofthe gene-drug associations, since these required the information contained in pairs of genes; this was the case for all three correlation measures. Overall, the use of our combination of linear, nonlinear, ID and 2D methods allowed for the discovery of 239 marker genes for high drag sensitivity, while sole reliance on the linear ID method, LDA ID, would have yielded only 43 markers, or fewer than 20% ofthe total.

Each ofthe six methods identified gene-drug correlations not found by any ofthe other five methods. LDA ID yielded only five gene markers not identified by at least one of the other methods. For QDA ID, 1 gene was found by this method only. Uniform gaussian ID was the most effective ofthe ID methods in this respect, yielding 9 genes correlated with high sensitivity found by this method only. By contrast, genes peculiar to each 2D method included (in pair combinations) 52 genes for LDA, 32 genes for QDA, and 49 genes for uniform/Gaussian.

An example ofthe 2D approach is diagrammed in Fig. 3. Expression levels ofthe gene elongation factor TU are plotted vs. expression levels ofthe gene SID W 116819 for the 60 cell lines, whose sensitivities to fluorodopan varied. The areas mapped out by the Gaussian distributions separate most ofthe black (filled-in squares) points (highly sensitive) cell lines from the white (open squares) points (low sensitivity) cell lines, placing them in separate regions ofthe graph. Twelve cell lines with high sensitivity to fluorodopan (black points) had varying levels of expression for both genes 1 and 2. In Fig. 3, for either SID W 116819 or elongation factor TU alone, below zero (-) expression occurs in both high and low sensitivity cell lines; similarly, above zero (+) expression for each gene alone occurs in both high and low sensitivity cell lines. Therefore, neither gene alone correlates with sensitivity. However, the genes can be used in combination to obtain a correlation between gene expression and high drag sensitivity. Cell lines that are highly sensitive to fluorodopan (black points) tend to have greater than zero expression values for both genes (+ +), or below zero expression values for both genes (- -), while the combinations (+ -) and (- +) tend to occur in cell lines that have low sensitivity to fluorodopan (white points).

(The use of + and - here is an oversimplification to describe the general distribution of black and white points on the graph in Fig. 3.)

Figs. 3 through 6 depict 2D analysis of gene expression-drag sensitivity data for 60 cancer cell lines. Fig. 3 employs QDA analysis. Each point represents a cell line, with its location specified by the relative expression of two genes (x and y coordinates). The points are coloured by the cell line's response to Fluorodopan. The contours represent points of equal probability as predicted by the methods described herein. In general the areas where black squares tend to be concentrated are areas of predicted high sensitivity. The arrows indicate the direction of predicted increasing sensitivity. The outermost contour to the bottom left and top right show the decision surface generated by the two Gaussian distributions: outside the outermost contour are classified as high response and the between the gradients as low response. Expression levels of SID W 116819 alone are uncorrelated with sensitivity because a plus (+) can correspond to either high or low sensitivity, and a minus (-) can correspond to either high or low sensitivity; the same is true of elongation factor TU. However, as shown in Table 4 below, when either (+) or (-) co-occurs in both genes, sensitivity is high. When expression levels of SID W 116819and elongation factor TUhave opposite signs, sensitivity is low. We therefore obtain a rule for the correlation of the pair of genes with fluorodopan sensitivity.

Table 4

Other examples for the 2D methods are shown in Figs. 4, 5 and 6, and their respective Tables 5, 6 and 7 below.

Referring to Fig. 4, according to LDA 2D method, both SID W 242844 and SID W 26677 are needed to predict high sensitivity to mitozolamide. For SID W 242844alone, (+) is associated with low sensitivity only, while (-) can be associated with low or high sensitivity. For SID W 26677, (-) is always associated with low, and (+) can correspond to either high or low sensitivity. However, the combination (- +) corresponds to high sensitivity only, so both genes are needed to establish a correlation with high sensitivity. Table 5

Referring to Fig. 5, according to QDA 2D method, both SID W 242844 and ZFP36 are needed to predict high sensitivity to mitozolamide. For SID W 242844, (-) can correspond to either high or low sensitivity, and (+) corresponds to low sensitivity. For ZFP36, (-) corresponds to either high or low, and (+) corresponds only to low sensitivity. However, the combination (- -) corresponds only to high sensitivity, so both genes are needed for the correlation.

Table 6

Referring to Fig. 6, according to uniform/gaussian 2D, for the high sensitivity cell lines, expression of SID W 242844 tends to be negative (-), while expression of ESTs Chr.l 488132 tends to be positive (+). Both SID W 242844and human nucleotide binding protein are needed to predict high sensitivity to mitozolamide. For SID W 242844, (+) is always associated with low sensitivity, and (-) can be associated with either high or low. For ESTs Chr.l 488132, (-) is associated only with low, and (+) can correspond to either high or low. The combination (- +), however, is associated with high, while all other combinations predict low sensitivity. Therefore, both genes are needed to predict high sensitivity.

Table 7

Many ofthe results could not be classified easily as simple plus/minus distributions, but the concept of requiring a particular range of expression value combinations for each pair of genes applies in all cases shown for the 2D methods. In some cases, this range of values includes zero (no deviation in expression from mixed culture control). This is acceptable, since we are interested only in relative basal gene expression levels, not perturbed gene expression relative to the control. For example, a combination of approximately zero (0) expression for gene SID 289361 and positive (+) expression for gene SID 327435 correlated with high sensitivity to fluorouracil according to QDA 2D, in one case.

The ID approach is shown in Figs. 7 and 8. For single gene correlations, only the value on the x-axis (horizontal axis) is considered. A random variable was used to create a y- axis (vertical axis) as a visual aid to avoid the problem of overlapping points. Referring to Fig. 7, according to LDA ID, cell lines with high sensitivity to mitozolamide exhibited high levels of PTN expression. Referring to Fig. 8, Uniform/gaussian ID determined that cells with high sensitivity to mitozolamide expressed DOC-2 mitogen- responsive phosphoprotein in a particular range of values above control. Random variable on y-axis permits visualization of data points that would obscure one another in a one-dimensional graph.

In some instances, we found significant correlations between a gene and more than one drag. Generally, the drags that correlated with a gene were from the same class, however, this was not always the case. Results are shown in previously set out Table 3. We determined that certain levels of expression for specific genes are consistently associated with high sensitivity to drags for cancer in 60 human cancer cell lines. Linear analysis methods alone were insufficient to identify many statistically significant correlations between basal gene expression and high sensitivity to drags. In addition, we have demonstrated the need for 2D methods, as in many cases, combinations of genes contain the information required to establish correlations with drag sensitivity. This suggests that the physiological functions of cancer cells are often governed by the synergistic actions of multiple genes. These results are consistent with the idea that physiological systems are by nature complex, nonlinear systems, and should be analysed as such.

As shown in Table 3 (where Bayes mixture refers to the Uniform/Gaussian), every one ofthe six example methods, LDA, QDA, and Uniform/Gaussian each for ID and 2D analyses, identified gene-drag correlations not discovered by any ofthe other five methods. This is especially true for the 2D methods. A combination of correlation techniques is appropriate for efficient interpretation of DNA microarray data.

The variability of cancer cell types poses two interrelated problems: 1) diagnosis, and 2) choice of treatment. Evidence has been found that the gene expression patterns of breast-derived cancer cell lines reflect those ofthe normal tissue of origin and of a breast-derived tumor, suggesting that cell lines may be useful in determining the gene expression patterns of in vivo cancer cells. If this is the case, it should be possible to use the results of large-scale studies of gene expression and drag responses in cancer cell lines to create databases of diagnostic markers for various cancers. Linear, nonlinear, and combinatorial analyses could be applied to determine those markers, and to suggest, appropriate therapeutic drags. As we have demonstrated in the present study, the use of nonlinear and combinatorial analyses in addition to linear, single-gene methods, increases the number of gene-drug associations, and therefore should improve the probability of determining appropriate drag therapies.

Markers identified by these computational methods could be used as the basis for diagnostic tests specific for those genes, perhaps in the form of smaller-scale microarray assays. Tests such as these would be aimed directly toward determination ofthe best choice(s) for therapeutic drag treatment. For example, a diagnostic test indicating high expression levels for both genes elongation factor TU and SID W 116819 (Fig. 3) would suggest a high probability of a response to fluorodopan treatment.

The present study focused on basal gene expression patterns as indicators of drag sensitivity. In carrying out the embodiment described above for the NCI60 dataset, we computationally distinguish strong from weak biological responses (i.e., to discriminate, classify, or predict biological responses). In its details, the method employs computationally-derived associations between computationally-analyzed quantitative gene expression data and computationally-analyzed quantitative intensity data. The intensity data represents observables (other than gene expression) assumed to be related in some arbitrary, but graded, manner to the biological responses.

We used a "biological response scoring function, " called f , where / : U — R¹ c [0,1] , and U is a 1 -parameter continuous path in R^m , m > 1. /^" is constructed to represent biological response on a bounded ordinal scale of real numbers, where / = 0 is interpreted to mean "no or negligible biological response";

f — 1 is interpreted to mean "very substantial, strong, or high biological response";

' is interpreted to mean "biological response somewhere between negligible and substantial in proportion to proximity to 0 or 1, respectively."

Formally, the domain U of f is defined to be a 1 -parameter continuous path in m- dimensional space. E.g., U can simply be scalar, i.e., U c R^l ; or U can be an arbitrary 1-parameter path through higher-dimensional space R^m, m > l (e.g., a series of m- dimensional feature vectors indexed by continuous time). Note: The examples provided here concentrate on the scalar domain case ( i.e., U c R¹ ), but the approach also applies to cases of higher-dimensional continuous 1-parameter paths. Domain U cz R¹ is interpreted to mean:

"degree or intensity of external effect on the biology " either on an increasing or decreasing scale.

Examples: U represents drag dose (absolute concentration or dose relative to some standard dose) along an increasing, or decreasing, scale;

U can represent the dose of drag which causes half-maximal cellular growth rate as charted along a scale which decreases to the right; U represents - logarithm ₁₀ (dose) , where dose is the dose which yields half- maximal total cell mass accumulating in a chemostat under otherwise standard conditions (e.g., let r c U such that r = -logGI50 = -logarithm ₁₀(GI50) , where

GI50 = drag dose which yields 50% ofthe cellular mass which is achieved under some standard untreated-with-drug conditions. Note that in this last example, r increases as GI50 decreases. In this case, an increasing r represents a decreasing "intensity of dose needed to obtain some defined biological effect."

The function f assigns a readily interpretable numerical "biological response score" in the continuous interval [0, 1 ] to a "degree or intensity of external effect on biology" from a scale U <z R^l . Thus, f is what inexorably links "intensity of external effect on biology" to a readily interpreted biological response scale, where the interpretations of f values are given in la) above.

Example (continuous piece- wise linear biological scoring function):

0, r < 0.7 Let /(r) = (r - 0.7)/0.3, r e [0.7,1) , where r = -logGI50 = -logarithm₁₀(GI50) . l, r ≥ l

Interpretations:

If the dose required to achieve some biological effect (say, 50% growth inhibition) is small, then score this phenomenon as "strong biological response", i.e., "cells are very sensitive." In f(r) terms, if GI50 < 0.1 (i.e.,

-log(GI50) > l ), then f = \ . If the dose required to achieve some biological effect (say, 50% growth inhibition) is large, then score this phenomenon as "weak biological response", i.e., "cells are very insensitive." In f( ) terms, if GI50 > 0.2 (i.e., -log(GI50) < 0.7 ), then / = 0.

If the dose required to achieve some biological effect (say, 50% growth inhibition) is modest or a some gradation between low and high, then score this phenomenon as "mixed-strength biological response", i.e., "cells are somewhat sensitive and/or somewhat insensitive." In f(r) terms, if 0.2 > GI50 > 0.1 (i.e.,

0.7 < -log(GI50) < 1), then / = (r -0.1)/ 0.3.

Example (smooth biological scoring function):

Let ,/«»«('^•) r ≥ a, b > a ≥ 0, v > l ,

where r = - log GI50 = - logarithm ₁₀ (GI50)

Let: i denote, or label, any given external effect, or situation, on the biology, e.g., temperature, pH, therapeutic intervention, compound applied, drug dosed, etc. (For explanatory convenience, for now on we often refer to any external effect on the biology as "drug.")

denote any biological source of gene expression data, e.g., patient, tissue, cultured cell line, etc. (For explanatory convenience, for now on we often refer to any biological source of expression data as "cell line.") k denote, or label, any given gene, mRNA species, gene product, or protein. (For explanatory convenience, for now on we often refer to any of these entities as "gene.")

g denote, or label, gene abundance or expression level, however numerically adjusted or normalized, of gene k in cell line .

a represent, or label, any desired categorical description of biological response score. E.g., a = any of "high", "strong", "sensitive/insensitive", etc. if f =1 ; e.g. a — any of "low", "weak", "insensitive", etc., if / = 0 ; e.g., a - any of "middle", "modest", "mixed sensitive\insensitive", etc. if 0 < f < 1.

w represent, or label, generally the biological response score (i.e., f value) of any biological source under any external effect or situation, e.g., the sensitivity of a cell line to a drag.

w'^,J specifically denote, or label, the biological response score (i.e., f value) of biological source /^* under any external effect or situation i , e.g., f value of cell line under some specified exposure to drug i .

HY specifically denote, or label, the biological response score (i.e., f value) which falls in some particular category a (e.g., a - sensitive) of biological source / under any external effect or situation i , e.g., w;'_e ^J _nsitlve means the f value is 1 for cell line under some specified exposure to drug i .

C_a' denote the set of biological sources falling in biological response category a when the biological source is external effect i . E.g.,. C_s'_ensitive is the set comprising cell lines for which the respective f values are 1 when exposed to drag i at some specified dose, i.e., the set of cell lines sensitive to drag / . C' denote the cardinality of C_a' , i.e., the number of elements in set C_a' . E.g.,

_{' t}r_ftfve ⁼ 23 , means that for the collection of cell lines considered, there are 23 cell lines that are sensitive to drag i .

For any given external biological effect (e.g., drug i administered by some specified dosing regime), and for any gene k , ...

Compute a category-wise data-summarizing mathematical, statistical, machine learning-based, data mining-based, or empirical, etc. entities. For example:

Compute histogram comprising g , for given k , for e C' . E.g., histogram of abundances of gene k from all the cell lines sensitive to drug i .

Compute parameters necessary to fit any chosen mathematical density function or continuous curve to a a category-wise histogram ofthe type described in 3a.l above. E.g., in preparation for fitting a gaussian distribution to {g(}, j e C s.ensitive ' compute parameters that are the cell line sensitivity-weighted gene k sample

mean _!.g ^rø"^/ve and variance s² ,g ^nsUtM , where

—sensitive _ ij j /^"O i,j • f-Λ iSk - ^W gk / ∑ ' J ^e sensitive I _^sensitive .. L i j I — \ l \ ' ,.,' ,■ _^- r~<'

S tSk = ^W {gi - iSk ) /∑ ^W > J ^ ^Csen_sitive

Compute a category-wise average data-summarizing parameters. E.g., sensitive\insensitive average variance are, respectively,

(s² _igk) = (s² _igr^itive∑w^{i '} +s² v^j) ,

where j' ≡ C_s'_ensUive and j C _ensme

σ ^g = the square root of the average variance.

For all a categories of interest, compute a category-wise data-summarizing mathematical, statistical, machine learning-based, data mining-based, or empirical, etc. entities based on any ofthe a category-wise average data-summarizing parameters such as those examples described above. For example: Compute a gaussian summarizing entity .Gf^ϊ"^,vβfor gene k in the cell lines sensitive to drag t , i.e., _iGr^mve(g,μ,σ) = (σ^y¹ _Qχp(-(g-μ)²/(2σ²)) where sensitive μ = ,$-"^{* e} and σ = ^ and compute analogous _tG_} insensitive

Compute discriminators, classifiers, and predictors of a , the category- wise biological response to external event i , but based on information computed from a given gene k . In these computations, we employ as needed any ofthe preparatory computations described above. For example: .

Compute a Bayesian probability P(j e C_a' \g() that a cell line / is in biological response category a due to biological effect , given the gene k abundance in cell line / , e.g.,

fi (si) = probability of abundance value g_k' from the gaussian density fitted to the histogram ofthe gene k abundances over the cell lines in response category a when subjected to biological effect .

A probability difference for the above probability is also computed, e.g.,

difference Bayesian U e C?

- W^>>/Y w J ε C" .

Note: Importantly, differ ence_Bayesim is the difference between 'the predicted probability that cell line is in the category a as computed from the gene k abundances across cell lines ' and 'the Observed probability that cell line / is in category a as computed from the effects of biological effect i on the cell lines'.

As described below the determination ofthe likelihood of a co-occurrence was calculated using a number of differing methods, namely: Uniform\Gaussian Discriminant Analysis - 1 -dimensional (UGDA ID) Uniform\Gaussian Discriminant Analysis - 2-dimensional (UGDA 2D) Linear Discriminant Analysis - 1 -dimensional (LDA ID) Quadratic Discriminant Analysis - 1 -dimensional (QDA ID) Linear Discriminant Analysis - 2-dimensional (LDA 2D) Quadratic Discriminant Analysis - 2-dimensional (QDA 2D)

Uniform\Gaussian Discriminant Analysis - 1 -dimensional (UGDA ID)

This method computes a Bayesian conditional probability P(j e c?^e"^s'"^ve | g{)

10 that a cell line j is sensitive to drug i , given the gene k abundance g[ in cell line f .

The probability is computed using the following equation: fisensitive s j _ p, ^sensitive

P(j e Cr^llve \ gi) =- G sensitive j _\ ns _/^sensitive _\ , ₇- ₇- / „. i_\ rjs ^insensitive _\ , k (g_k ⁾ - P(^ci ⁾+i^uΛgk) - (^ci )

15 where

p(C°^ensi,ive) = prior probability ofthe sensitive set

P Ci"^se"^smve) =prior probability ofthe insensitive

/ /-> x I ^insensitive 1 s-tsensitive 1 , 1 -ιinsensitive

_tG_t'^emUtve (g() = probability of abundance value g[ from the gaussian density fitted to the histogram ofthe gene k abundances over the sensitive cell lines when subjected to drag i .

25 /2(σf')²

where μ_k ^sen = mean of gene k abundances in the sensitive cell lines, j e C_s'_ensillve

σ_k ⁿ= standard deviation of gene k abundances in the sensitive cell lines,

J sensitive

i U_k (g( ) = probability of abundance value g[ from the uniform density fitted to the gene k abundances over all cell lines when subjected to drag . For a given gene k, this value is constant across all cell lines,./, i.e.,

ma fe -mintø_t) where ax(g_k) = maximum abundance of gene k over all cell lines mm.(g_k) = minimum abundance of gene k over all cell lines

Sample parameters for the UGDA ID for the NCI60 dataset are: Rule 1 Gene: SID W 376472 Homo sapiens clone 24429 mRNA sequence [5':AA041443 3':AA041360] ^'

Drug: Inosine-glycodialdehyde Parameters: μ_k ^sen = -0.4394, σ_k ^sen = 0.4217 iU_k(g_k ^j) = 0.2538

P(Q^sensitive) = 0.1978, p(C_i ^insensitive) = 0.8022

Rule 2

Gene: Human clone 23665 mRNA sequence Chr.17 [488020 (IW) 5':AA054745 3':AA054747]

Drug: Dolastatin-10 Parameters: μ_k ^sen = -0.7752, σ_k ^sen = 0.3685 iU_k(g_k ^j) -= 0.2347 p_(C.sensitive_{) =} Q ^ p^.insensitiv^ _{= Q g65} Rule 3

Gene: SID W 469272 Epidermal growth factor receptor [5':AA026175 3':AA026089] Drag: Dichloroallyl-lawsone Parameters: μ_k ^sen = -0.2886, σ_k ^sen = 0.4416 iU_k(g_k ^j) *= 0.2299

P(Cj^sensitive) = 0.2172, p( ^in∞Mitiw) = 0.7828

Rule 4

Gene: ESTs Chr.l [488132 (IW) 5':AA047420 3':AA047421]

Drug: N-phosphonoacetyl-L-aspartic-ac

Parameters: μ_k ^sen = 0.2863, σ_k ^sen = 0.3651 iU_k(g_k ^j) = 0.241

P(Ci^sensitive) = 0.2583, p(c_i ^insensitive) = 0.7417

Rule 5

Gene: LBR Lamin B receptor Chr.l [307225 (IW) 5':W21468 3':N93426] Drag: Pyrazofurin Parameters: μ_k ^sen = 0.4077, σ_k ^sen = 0.4993 iU_k(g_k ^j) = 0.237

P(Cι^sensitive) = 0.2594, p(c_i ^insensitive) = 0.7406

Rule 6

Gene: SID W 305455 TRANSCRIPTIONAL REGULATOR ISGF3 GAMMA SUBUNIT [5':W39053 3':N89796] Drag: Cyanomorpholinodoxorubicin Parameters: μ_k ^∞" = 0.4419, σ_k ^sen = 0.3503 iU_k(g_k ^j) = 0.2326

P(Cj^sensitive) = 0.2067, p(C_i ^insensitive) = 0.7933 Rule 7

Gene: SID 429145 Human nicotinamide N-methyltransferase (NNMT) mRNA complete cds [5': 3':AA004839] Drug: Semustine (MeCCNU) Parameters: μ_k ^sen = 0.2891, σ_k ^sen = 0.398 iU_k(g_k ^j) = 0.3155

P(Q^sensitive) = 0.1606, P(c_i ^insensitive) = 0.8394

Rule 8

Gene: SID W 242844 ESTs Moderately similar to ! ! ! ! ALU SUBFAMILY J WARNING ENTRY !! ! ! [H.sapiens] [5':H94138 3':H94064] Drag: Mitozolamide Parameters: μ_k ^sen = -1.008, σ_k ^sen = 0.5668 iU_k(g_k ^j) = 0.2381

P(Ci^sensitive) = 0.2006, p(C_i ^insensitive) = 0.7994

Rule 9

Gene: *Homo sapiens lysosomal neuraminidase precursor mRNA complete cds SID W 487887 Hexabrachion (tenascin C cytotactin) [5*:AA046543 3':AA045473] Drag: Mitozolamide Parameters: μ_k ^sen = 0.8444, σ_k ^sen - 0.5358 iU_k(g_k ^j) = 0.2597 P(Ci^sensitive) = 0.2006, p(Q^insensitive) = 0.7994

Rule 10 Gene: ESTs Chr.l [488132 (IW) 5*:AA047420 3':AA047421] Drag: Mitozolamide Parameters: μ_k ^sen = 0.4755, σ_k ^sen = 0.3355 iU_k(g_k ^j) = 0.241 _p(C.sensitiv_{e) Q 7994}

Rule 11 Gene: Human mitogen-responsive phosphoprotein (DOC-2) mRNA complete cds Chr.5 [428137 (IE) 5': 3*:AA001933] Drag: Mitozolamide Parameters: μ_k ^sen = 0.3967, σ_k ^sen = 0.3587 iU_k(g_k ^j) = 0.2342

P(Q^sensitive) = 0.2006, P(C_i ^insensitive) = 0.7994

Rule 12

Gene: SID W 345420 Homo sapiens YAC clone 136A2 unknown mRNA 3 'untranslated region [5':W76024 3':W72468] Drag: Mitozolamide Parameters: μ_k ^sen = 0.7456, σ_k ^sen = 0.5579 iU_k(g_k ^j) -= 0.2625

0.7994

Rule 13

Gene: CDH2 Cadherin 2 N-cadherin (neuronal) Chr. [325182 (DIRW) 5':W48793 3':W49619] Drug: Mitozolamide Parameters: μ_k ^sen = 0.6581, σ_k ^sen = 0.3744 iU_k(g_k ^j) = 0.2564

P(Ci^sensitive) = 0.2006, P(C_i ^insensitive) = 0.7994

Rule 14

Gene: SID W 280376 ESTs Highly similar to CELL CYCLE PROTEIN KINASE

CDC5/MSD2 [Saccharomyces cerevisiae] [5':N50317 3':N47107] Drag: Mitozolamide Parameters: μ_k ^sen = 0.7347, σ_k ^sen = 0.4233 iU_k(g_k ^j) = 0.177 _P(_C.^sens^itiv_{e) =} 0.2006, P(c_i ^insensitive) = 0.7994

Rule 15

Gene: Human mRNA for reticulocalbin complete cds Chr.l 1 [485209 (IW) 5':AA039292 3*:AA039334] Drag: Cyclodisone Parameters: μ_k ^sen = 0.6598, σ_k ^sen = 0.2562 iU_k(g_k ^j) = 0.1672

P(Ci^sensitive) = 0.1689, p(c ^ensitive) = 0.8311

Rule 16

Gene: SID W 345420 Homo sapiens YAC clone 136A2 unknown mRNA 3'untranslated region [5':W76024 3':W72468] Drag: Clomesone Parameters: μ_k ^sen = 0.7165, σ_k ^sen = 0.4934 iU_k(g_k ^j) = 0.2625 p_(c.sensitiv_{e) = i 917j = Q} g₀₈₃

Rule 17

Gene: SID 289361 ESTs [5*:N99589 3':N92652]

Drag: Fluorouracil (5FU)

Parameters: μ_k ^sen = 0.03614, σ_k ^sen = 0.186 iU_k(g_k ^j) = 0.2252 _p(c.sensitive_{) = Q} -^ p^.insensitive^ ₌ Q ^^

Rule 18 Gene: SID 43555 MALATE OXIDOREDUCTASE [5':H13370 3':H06037]

Drug: Fluorouracil (5FU)

Parameters: μ_k ^sen = 0.9686, σ_k ^sen = 0.4053 iU_k(g_k ^j) = 0.241 p_(c.sensitiv_e)

Rule 19

Gene: H.sapiens mRNA for Gal-beta(l-3/l-4)GlcNAc alpha-2.3-sialyltransferase Chr.l 1 [324181 (IW) 5':W47425 3':W47395] Drag: Fluorouracil (5FU) Parameters: μ_k ^sen = -0.3532, σ_k ^sen = 0.2383 iU_k(g_k ^j) = 0.2488 p_(C.sensitive_{) = 0# 1628j} p^.insensitive) _{= Q g3J2}

Rule 20

Gene: ESTs Moderately similar to ZINC-BINDING PROTEIN A33 [Pleurodeles waltl] Chr.16 [25718 (RW) 5':R12025 3':R37093] Drag: Fluorodopan Parameters: μ_k ^sen = -0.542, σ_k ^sen = 0.2812 iU_k(g_k ^j) = 0.2079

P(Ci^Sensitive) = 0.2061, p(C_i ^insensitive) = 0.7939

Rule 21

Gene: SID 470501 ESTs [5':AA031743 3':AA031652] Drag: Asaley ^• Parameters: μ_k ^sen = -0.7867, σ_k ^sen = 0.4327 iU_k(g_k ^j) = 0.1869

P( ^sensitive) = 0.1878, p( ^insensitive) = 0.8122 Rule 22

Gene: SID 307717 Homo sapiens KIAA0430 mRNA complete cds [5': 3':N92942] Drug: Cyclocytidine Parameters: μ_k ^sen = 0.004825, σ_k ^sen = 0.232 iU_k(g_k ^j) = 0.1835

P(C;^sensitive) = 0.2533, p(C_i ^insensitive) = 0.7467

Rule 23 Gene: SID W 122347 ESTs [5':T99193 3*:T99194] Drag: Oxanthrazole (piroxantrone) Parameters: μ_k ^sen = -0.9888, σ_k ^sen = 0.6153 iU_k(g_k ^j) = 0.2198 p(_C.sensitiv_{e) = 0 1956; 0 gQ44}

Rule 24

Gene: SID W 429290 ESTs [5':AA007457 3':AA007361] Drag: Oxanthrazole (piroxantrone) Parameters: μ_k ^sen = 0.6229, σ_k ^sen = 0.3177 iU_k(g_k ^j) = 0.2532 p_(c.sensitiv_{e) = i 956j = Q 4}

Rule 25

Gene: ALDOC Aldolase C fructose-bisphosphate Chr.17 [229961 (IW) 5':H67774 3':H67775]

Drag: Anthrapyrazole-derivative Parameters: μ_k ^sen = -0.2373, σ_k ^sen = 0.3786 iU_k(g_k ^j) = 0.2049 P(Cj^sensitive) = 0.2006, p(cr^enstive) = 0.7994 Rule 26

Gene: SID W 381819 Plastin 1 (I isoform) [5':AA059293 3':AA059061] Drug: Teniposide Parameters: μ_k ^sen = 0.05147, σ_k ^sen = 0.3839 iU_k(g_k ^j) = 0.2101

P(Ci^Sensitive) = 0.1894,

= 0.8106

Rule 27 Gene: SID W 345683 ESTs Highly similar to INTEGRAL MEMBRANE

GLYCOPROTEIN GP210 PRECURSOR [Rattus norvegicus] [5':W76432 3':W72039]

Drag: Daunorabicin

Parameters: μ_k ^sen = 0.918, σ_k ^sen = 0.3704 iU_k(g_k ^j) = 0.2762

P( ^sensitive) = 0.1811, p(Q^insensitive) = 0.8189

Rule 28

Gene: SID 234072 EST Highly similar to RETROVIRUS-RELATED POL POLYPROTEIN [Homo sapiens] [5': 3':H69001 ] Drag: Aphidicolin-glycinate Parameters: μ_k ^sen = -0.3626, σ_k ^sen = 0.4252 iU_k(g_k ^j) = 0.207 p(_C.sensitive^ ₌ Q _1994j p^.insensitive^ _{= Q gQQ6}

Rule 29

Gene: SID 50243 ESTs [5*:H17681 3':H17066] Drug: CPT,10-OH Parameters: μ_k ^sen = 0.8677, σ_k ^sen = 0.5387 iU_k(g_k ^j) = 0.2653

P( ^sensitive) = 0.1856, P(Q^insensitive) = 0.8144 Rule 30

Gene: SID W 346587 Homo sapiens quiescin (Q6) mRNA complete cds [5':W79188 3':W74434] Drag: CPT,10-OH Parameters: μ_k ^sen = 1.001, σ_k ^sen = 0.6123 iU_k(g_k ^j) = 0.2358

P(C;^sensitive)

= 0.8144

Rule 31

Gene: SID W 361023 ESTs [5':AA013072 3':AA012983] Drag: CPT,10-OH Parameters: μ_k ^sen = -0.8339, σ_k ^sen = 0.6084 iU_k(g_k ^j) = 0.2222

P(Ci^sensitive) = 0.1856, p(c_i ^insensitive) = 0.8144

Rule 32 Gene: SID W 488148 H.sapiens mRNA for 3'UTR of unknown protein [5': AA057239

3':AA058703]

Drag: CPT

Parameters: μ_k ^sen = 0.8224, σ_k ^sen = 0.5588 iU_k(g_k ^j) = 0.2577 _p(c.sensitiv_{e) = 0}_2594₅ p(_C.insensitiv_{e) = Q ?4()6}

Rule 33

Gene: SID W 159512 Integrin alpha 6 [5':H16046 3':H15934] Drag: CPT Parameters: μ_k ^sen = 0.7291, σ_k ^sen = 0.6557 iU_k(g_k ^j) -= 0.2571 P(Ci^sensitive) = 0.2594,

= 0.7406

Rule 34

Gene: SID W 429290 ESTs [5*:AA007457 3':AA007361] Drag: CPT Parameters: μ_k ^sen = 0.7084, σ_k ^sen = 0.4576 iU_k(g_k ^j) = 0.2532

P(C_; ^sensitive) = 0.2594, p(c_i ^insensitive) = 0.7406

Rule 35

Gene: ESTs Chr.5 [487396 (IW) 5':AA046573 3':AA046660] Drag: CPT Parameters: μ_k ^sen = 0.6068, σ_k ^sen = 0.3836 iU_k(g_k ^j) = 0.1848

P(Ci^sensitive) = 0.2594, p(c_i ^insensitive) = 0.7406

Rule 36 Gene: SID W 361023 ESTs [5':AA013072 3*:AA012983] Drag: CPT,20-ester (S) Parameters: μ_k ^sen = -0.6333, σ_k ^sen = 0.554 iU_k(g_k ^j) = 0.2222 p(_C.sensitiv_{e) = 0 255j} p^.insensitive) _{= Q 45}

Rule 37

Gene: SID W 125268 H.sapiens mRNA for human giant larvae homolog [5':R05862 3':R05776] Drag: CPT,20-ester (S) Parameters: μ_k ^sen = - 0.4871, σ_k ^sen = 0.5365 iU_k(g_k ^j) = 0.266 P(Cj^sensitive) = 0.2844, p(C_i ^insensitive) = 0.7156

Rule 38

Gene: SID W 361023 ESTs [5':AA013072 3':AA012983] Drug: CPT,20-ester (S) Parameters: μ_k ^sen = -0.608, σ_k ^sen = 0.5756 iU_k(g_k ^j) = 0.2222

P(C;^sensitive) = 0.2844, p(c_i ^insensitive) = 0.7156

Rule 39

Gene: SID W 125268 H.sapiens mRNA for human giant larvae homolog [5':R05862 3':R05776] Drug: Chlorambucil Parameters: μ_k ^sen = -0.4569, σ_k ^sen = 0.4595 iU_k(g_k ^j) = 0.266

P(Q^sensitive) = 0.2206, p(c ^{eBsiύ e}) = 0.7794

Rule 40

Gene: SID 381780 ESTs [5*:AA059257 3':AA059223]

Drug: Paclitaxel — Taxol

Parameters: μ_k ^sen = 0.1618, σ_k ^sen -= 0.1828 iU_k(g_k ^j) = 0.2053

P(Q^sensitive) = 0.1622, p(Q^insensitive) = 0.8378

Uniform\Gaussian Discriminant Analysis - 2-dimensional (UGDA 2D)

This method computes a Bayesian conditional probability P(j e C*^e'^mtιve \ g_k ^J,gj) that a cell line j is sensitive to drug i , given the abundances of two genes k and

I, g and g[, respectively, in cell line /^'. The probability is computed using the following equation:

where p Q^sensitive- ₌ prior probability ofthe sensitive set

C? i /(i c \ + a insensitive

\) , _{= prior pro}b_atøi_ity of the insensitive

set= c; insensitive \ c; sensitive + \ Q insensitive

I), i G_k l'^sitive (g{ , gj ) = joint probability of abundance values gl and gj from the bivariate gaussian density fitted to the histogram of gene k and / abundances over the sensitive cell lines when subjected to drag i .

where μ_k ⁿ = mean of gene k abundances over the sensitive cell lines

σ_k ⁿ = standard deviation of gene k abundances in the sensitive cell lines

^μ u^ιsen - mean of gene / abundances over the sensitive cell lines rr ^en ¹ = standard deviation of gene / abundances in the sensitive cell lines

-.sen

P^k,ι = correlation coefficient of gene k and gene / abundances in the sensitive cell lines _!.C/_{t /}(g ,g ) = probability of abundance values gl and gj from the uniform density fitted to gene k and gene / abundances over all cell lines when subjected to drag /. For given genes k and /, this value is constant across all cell lines,/.

' *'' ^k' ' [m x(g_k) - .m(g_k)] ^ [m x g_! ) - in(g_l )] ^, where max(gy_t) ⁼ maximum abundance of gene k over all cell lines m.(g_k) = minimum abundance of gene k over all cell lines max(gv) = maximum abundance of gene / over all cell lines min(g_/) = minimum abundance of gene / over all cell lines

Sample parameters for the UGDA 2D on the NCI60 dataset are:

Rule l

Gene 1: SID W 116819 Homo sapiens clone 23887 mRNA sequence [5':T93821 3':T93776]

Gene 2: SID W 484681 Homo sapiens ES/130 mRNA complete cds [5':AA037568

3*:AA037487]

Drag: L-Alanosine

Parameters: μ_k ^sen = 0.006423, μι^sen = -0.25, σ_k ^sen = 0.7146, σι^sen = 0.4424, _Pk ^en = 0.7005

P(Ci^sensitive) = 0.2283, p(C_i ^insensitive) = 0.7717

Rule 2 Gene 1: EST Chr.6 [72745 (R) 5':T50815 3*:T50661]

Gene 2: ESTs Weakly similar to dual specificity phosphatase [H.sapiens] Chr.17

[488150 (IW) 5':AA057259 3':AA058704]

Drag: L-Alanosine

Parameters: μ_k ^sen = -0.3181, μι^sen = -0.4347, σ_k ^sen = 0.7029, σ,_. ^sen = 0.3548, _Pk>1 ^sen = 0.7733 iUfc g ^'i) ^ 0.03881

P(C_i ^sensitive) -= 0.2283, ^(ci ^^ve _{= 7717}

Rule 3 Gene 1 : SID W 469272 Epidermal growth factor receptor [5':AA026175 3':AA026089] Gene 2: MICA MHC class I polypeptide-related sequence A Chr.6 [290724 (R) 5': 3':N71782]

Drag: Dichloroallyl-lawsone Parameters: μ_k ^sen = -0.2886, μι^sen = -0.165, σ_k ^sen = 0.4416, σf¹¹ = 0.3495, P_k,ι^sen = 0.6631

^,₁^0 = 0.03649 P(Cι^sensitivέ) = 0.2172, p(c_i ^insensitive) = 0.7828

Rule 4 Gene 1 : PROBABLE UBIQUITIN CARBOXYL-TERMINAL HYDROLASE Chr.6

[129496 (E) 5':R16453 3':R14956]

Gene 2: SID W 125268 H.sapiens mRNA for human giant larvae homolog [5':R05862

3':R05776]

Drug: Dichloroallyl-lawsone Parameters: μ_k ^sen = 0.5512, μ_! ^sen = 0.1164, σ_k ^sen = 0.509, σι^sen = 0.7882, _Pk ^en = 0.8968

P(Ci^Sensitive) = 0.2172, p(Q^insensitive) = 0.7828

Rule 5

Gene 1 : Human LOT1 mRNA complete cds Chr.6 [285041 (I) 5': 3':N63378] Gene 2: UBE2H Ubiquitin-conjugating enzyme E2H (homologous to yeast UBC8) Chr.7 [359705 (DIW) 5*:AA010909 3':AA011300] Drug: DUP785-brequinar Parameters: μ_k ^sen *= 0.4687, μι^sen = -0.2413, σ_k ^sen = 0.5604, _σι ^sen = 0.6083, _Pk,ι^sen = -0.3827

P(Ci^sensitive) = 0.2694, p(Q^insensitive) = 0.7306 Rule 6

Gene 1 : Human putative 32kDa heart protein PHP32 mRNA complete cds Chr.8 [417819 (EW) 5*:W88869 3':W88662] Gene 2: SID W 305455 TRANSCRIPTIONAL REGULATOR ISGF3 GAMMA SUBUNIT [5':W39053 3':N89796] Drag: Pyrazofurin Parameters: μ_k ^sen = -0.2413, _μι ^sen = -0.01115, σ_k ^sen = 0.3564, _σι ^sen = 0.5233, _Pk ^en = -0.1372 ₁^(^0 = 0.04906

P(Cj^sensitive) = 0.2594, p(c_i ^insensitive) = 0.7406

Rule 7

Gene 1 : SID W 509468 Protective protein for beta-galactosidase (galactosialidosis) [5':AA047117 3':AA047118]

Gene 2: SID W 214236 CD68 antigen [5':H77807 3':H77636]

Drag: Pyrazofurin

Parameters: μ_k ^sen = -0.3715, μr^en = -0.2611, σ_k ^sen = 0.521, σι^sea = 0.5311, _Pkjl ^sen = 0.8032 ^^^'0 = 0.05027

P(Q^sensitive) = 0.2594, p(Q^insensitive) = 0.7406

Rule 8

Gene 1: *Human ferritin L chain mRNA complete cds SID W 239001 ESTs [5*:H67076 3':H68158]

Gene 2: Homo sapiens mRNA for KIAA0638 protein partial cds Chr.l 1 [470670 (IW)

5':AA031574 3':AA031453]

Drag: Cyanomorpholinodoxorabicin

Parameters: μ_k ^sen = 0.438, μ_! ^{Sen ■}= 0.7537, σ_k ^sen = 0.507, σf" = 0.4528, _k,ι^sen = -0.7846

P(Ci^sensitive) = 0.2067, p(c_i ^insensitive) = 0.7933 Rule 9

Gene 1: IL8 Interleukin 8 Chr.4 [328692 (DW) 5':W40283 3*:W45324] Gene 2: SID W 305455 TRANSCRIPTIONAL REGULATOR ISGF3 GAMMA SUBUNIT [5':W39053 3':N89796] Drug: Cyanomorpholinodoxorubicin Parameters: μ_k ^sen = 0.856, μ_! ^sen = 0.4419, σ_k ^sen = 0.6623, _σι ^sen = 0.3503, _Pk ^en = -0.5992

P(Ci^sensitive) = 0.2067, p(c ^sensitive) = 0.7933

Rule 10

Gene 1: SID 272143 ESTs [5*: 3*:N35476]

Gene 2: SID W 345420 Homo sapiens YAC clone 136A2 unknown mRNA 3'untranslated region [5':W76024 3':W72468] Drug: Lomustine (CCNU) Parameters: μ_k ^sen *= 0.3141, μι^sen = 0.4027, σ_k ^sen = 0.5301, σfⁿ = 0.4267, _Pkjι^sen -= -0.9555 iU_k (g^jbgⁱ0 = 0-04943 p_(c.sensitiv_{e) = 0- 1067>} p_(C.msensitiv_{e) = Q} g^

Rule 11

Gene 1: ESTs Chr.l 1 [345012 (IW) 5':W76307 3':W72280] Gene 2: SID 429145 Human nicotinamide N-methyltransferase (NNMT) mRNA complete cds [5': 3':AA004839] Drag: Semustine (MeCCNU) Parameters: μ_k ^sen = 0.1845, μι^sen -= 0.2891, σ_k ^sen = 0.3375, σι^sen = 0.398, _Pk ^en = 0.6251 ιU _lι(gⁱ _k,g^,O = 0.06712 P(Ci^Sensitive) = 0.1606, p(c_i ^iπsensitive) = 0.8394

Rule 12

Gene l: INPPl Inositol polyphosphate-1 -phosphatase Chr.2 [183876 (EW) 5':H30231

3':H26976] Gene 2: SID 429145 Human nicotinamide N-methyltransferase (NNMT) mRNA complete cds [5*: 3':AA004839] Drag: Semustine (MeCCNU) Parameters: μ_k ^sen = 0.06554, μι^sen = 0.2891, σ_k ^sen= 0.5184, σι^sen = 0.398, _Pk ^en = -0.6708

^₁(^0 = 0.05885

P(Ci^sensitive) = 0.1606, P(C_i ^insensitive) = 0.8394

Rule 13 Gene 1: SID 276915 ESTs [5':N48564 3':N39452] Gene 2: SID 301144 ESTs [5':W16630 3':N78729] Drug: Mitozolamide Parameters: μ_k ^sen = 0.001165, μι^sen = 0.7785, σ_k ^sen = 0.4, σι^sen = 0.2994, _Pk)ι^sen = -0.3594 ^(^0 = 0.04824

P(Ci^sensitive) = 0.2006, p(c_i ^insensitive) = 0.7994

Rule 14

Gene 1: ESTs Chr.l [45747 (D) 5':H08940 3':H08856] Gene 2: Human mitogen-responsive phosphoprotein (DOC-2) mRNA complete cds Chr.5 [428137 (IE) 5': 3':AA001933] Drag: Mitozolamide Parameters: μ_k ^sen = -0.2316, μι^sen = 0.3967, σ_k ^sen = 0.4407, σf¹¹ = 0.3587, _Pk,ι^sen = -0.6006 _iU_k (g^l _k,g^l = 0.05485

P( ^sensitive) = 0.2006, p(c_i ^insenSitive) = 0.7994

Rule 15

Gene 1 : SID W 242844 ESTs Moderately similar to ! ! ! ! ALU SUBFAMILY J WARNING ENTRY ! ! ! ! [H.sapiens] [5*:H94138 3':H94064] Gene 2: ESTs Chr.l [488132 (IW) 5':AA047420 3*:AA047421] Drug: Mitozolamide Parameters: μ_k ^sen = -1.008, μι^sen = 0.4755, σ_k ^sen = 0.5668, σf¹ = 0.3355, _Pk)ι^sen = 0.3703

P(Ci^sensitive) = 0.2006, p(c_i ^insensitive) = 0.7994

Rule 1

^' Gene 1: ESTs Chr.l [488132 (IW) 5*:AA047420 3':AA047421] Gene 2: ESTs Chr.l [346583 (IRW) 5*:W79544 3':W74533] Drag: Mitozolamide Parameters: μ_k ^sen = 0.4755, μj^sen = 0.4998, σ_k ^sen = 0.3355, σι^sen = 0.593, _Pk,ι^sen = 0.612

P(Ci^sensitive) = 0.2006, p(c_i ^insensitive) = 0.7994

Rule 17 Gene 1: SID 276915 ESTs [5':N48564 3':N39452]

Gene 2: SID W 487878 SPARC/osteonectin [5*:AA046533 3*:AA045463]

Drag: Mitozolamide

Parameters: μ_k ^sen = 0.001165, μι^sen = 0.9224, σ_k ^sen = 0.4, σι^sen = 0.4976, _Pk ^en = -0.3656 iUy(g^l _k,gⁱι) = 0.04927

P(Cι^sensitive) = 0.2006, p(c_i ^insensitive) = 0.7994

Rule 18

Gene 1: *Human ferritin L chain mRNA complete cds SID W 239001 ESTs [5':H67076 3':H68158]

Gene 2: SID W 242844 ESTs Moderately similar to ! ! ! ! ALU SUBFAMILY J

WARNING ENTRY ! ! !! [H.sapiens] [5':H94138 3':H94064]

Drag: Mitozolamide

Parameters: μ_k ^sen *= 0.5746, μι^sen = -1.008, σ_k ^sen = 0.4099, _σι ^sen = 0.5668, _Pk ^en = 0.3637

p_(c.sensitiv_{e) = 0>20()6) p(c}.insensitive_{) = 0<7994} Rule 19

Gene 2: CDH2 Cadherin 2 N-cadherin (neuronal) Chr. [325182 (DIRW) 5':W48793 3*:W49619]

Drag: Mitozolamide Parameters: μ_k ^sen = 0.5746, μι^sen = 0.6581, σ_k ^sen = 0.4099, σι^sen = 0.3744, _Pk>fⁿ = -0.04564 iUyfeWi) - 0.05088 p(_C.^sensitiv _{e) = 0} 2006, p(c_i ^insensitive) - 0.7994

Rule 20

Gene 1 : SID 417008 ESTs Weakly similar to No definition line found [C.elegans] [5':

3':W87796] Gene 2: CDH2 Cadherin 2 N-cadherin (neuronal) Chr. [325182 (DIRW) 5':W48793

3':W49619]

Drag: Mitozolamide

Parameters: μ_k ^sen = 0.3847, μf^en = 0.6581, σ_k ^sen = 0.4824, σι^sen = 0.3744, _Pk ^en = 0.6278 iUyfei_fag-,) ----- 0.05309

P(Q^sensitive) = 0.2006, p(c_i ^iπsensitive) = 0.7994

Rule 21

Gene 1 : SID W 242844 ESTs Moderately similar to ! ! ! ! ALU SUBFAMILY J WARNING ENTRY ! ! ! ! [H.sapiens] [5*:H94138 3':H94064]

Gene 2: SID W 323824 NADH-CYTOCHROME B5 REDUCTASE [5':W46211

3*:W46212]

Drag: Mitozolamide

Parameters: μ_k ^sen = -1.008, μι^sen = 0.2421, σ_k ^sen = 0.5668, σfⁿ = 0.4385, _Pk,ι^sen = 0.04634

P(Ci^sensitive) = 0.2006, p(C_i ^insensitive) = 0.7994 Rule 22

Gene 1: SID 122022 [5':T98316 3':T98261]

Gene 2: *Homo sapiens lysosomal neuraminidase precursor mRNA complete cds SID W 487887 Hexabrachion (tenascin C cytotactin) [5':AA046543 3':AA045473] Drag: Mitozolamide Parameters: μ_k ^sen = 0.1567, μ,^sen = 0.8444, σ_k ^sen = 0.4277, σι^sen = 0.5358, _Pk,ι^sen = 0.6386

P(Ci^sensitive) = 0.2006, ^ 0^^ = 0.7994

Rule 23

Gene 1: SID W 488691 ESTs Highly similar to NODULATION PROTEIN G [Rhizobium meliloti] [5':AA045967 3':AA045833] Gene 2: ESTs Chr.7 [28051 (D) 5':R13146 3':R40626] Drag: Mitozolamide Parameters: μ_k ^sen = -0.4283, μι^sen = 0.6206, σ_k ^sen = 0.6985, σι^sen = 0.4756, _Pk,ι^sen = -0.9223 ^(^^'^0 = 0.05016

= 0.7994

Rule 24

Gene 1 : Human DNA sequence from clone 1409 on chromosome Xp 11.1 - 11.4. Contains a Inter-Alpha-Trypsin Inh Chr.X [485194 (I) 5':AA039416 3':AA039316] Gene 2: Human mRNA for reticulocalbin complete cds Chr.11 [485209 (IW) 5*:AA039292 3':AA039334] Drag: Cyclodisone Parameters: μ_k ^sen = 0.2487, μι^sen = 0.6598, σ_k ^sen = 0.4569, σι^sen = 0.2562, _Pk ^en = -0.4186 _iU_k (gⁱ _k,gⁱ0 = 0.03818 p(_C.sensitiv_{e) =} Q.1 ₆₈9, p( ^insβnsitive) = 0.8311

Rule 25

Gene 1 : Human mRNA for reticulocalbin complete cds Chr.l 1 [485209 (IW) 5':AA039292 3':AA039334] Gene 2: SID 147338 ESTs [5*: 3':H01302] Drag: Cyclodisone Parameters: μ_k ^sen = 0.6598, μι^sen = 0.1958, σ_k ^sen = 0.2562, σι^sen = 0.3673, _Pk ^en = -0.6593

p_(c.^sensitiv _{e) =} 0.1689, p( ^insensitive) = 0.8311

Rule 26 Gene 1 : Human GDP-dissociation inhibitor protein (Ly-GDI) mRNA complete cds Chr.12 [487374 (IW) 5':AA046482 3':AA046695]

Gene 2: Human mRNA for reticulocalbin complete cds Chr.l 1 [485209 (IW) 5*:AA039292 3':AA039334] Drag: Cyclodisone Parameters: μ_k ^sen = -0.2079, μι^sen = 0.6598, σ_k ^sen = 0.5996, σι^sen = 0.2562, _Pkjι^sen = -0.7022

P(Ci^sensitive) = 0.1689, p( ^insensitive) = 0.8311

Rule 27

Gene 1 : SID W 510182 H.sapiens mRNA for kinase A anchor protein [5': AA053156 3*:AA053135]

Gene 2: SID W 346663 ESTs [5':W94188 3':W74616] Drag: Cyclodisone Parameters: μ_k ^sen = -0.4516, μι^sen = 0.3877, σ_k ^sen = 0.4114, σf¹¹ = 0.3607, _PkjI ^sen = -0.8186 ^₁(^0 = 0.03563 p_(c.sensitiv_{e) = αl 6895 p(c}.insensitiv_{e) =} Q.8311

Rule 28

Gene 1: Homo sapiens clone 24560 unknown mRNA complete cds Chr.16 [418227 (IW)

5':W90284 30W9O6O7]

Gene 2: Human mRNA for reticulocalbin complete cds Chr.l 1 [485209 (IW) 5':AA039292 3*:AA039334] Drug: Cyclodisone Parameters: μ_k ^sen = 0.2463, μι^sen = 0.6598, σ_k ^sen = 0.3831, σι^sen = 0.2562, _Pk ^en = 0.5841 ^₁^0 = 0.03311

P(Cι^sensitive) = 0.1689, p( ^insensitive) = 0.8311

Rule 29

Gene 1: ESTs Chr.l [488132 (IW) 5':AA047420 3':AA047421] Gene 2: Human mRNA for reticulocalbin complete cds Chr.l 1 [485209 (IW) 5':AA039292 3':AA039334] Drug: Cyclodisone Parameters: μ_k ^sen = 0.479, μι^sen = 0.6598, σ_k ^sen = 0.3464, σι^sen = 0.2562, _k ^en = -0.4896

P( ^sensitive) = 0.1689, p( ^insensitive) = 0.8311

Rule 30

Gene 1: ESTs Chr.l [488132 (IW) 5*:AA047420 3*:AA047421] Gene 2: ESTs Chr.l [346583 (IRW) 5*:W79544 3':W74533] Drug: Cyclodisone Parameters: μ_k ^sen = 0.479, μι^sen = 0.4024, σ_k ^sen = 0.3464, σι^sen = 0.5961, _Pk,fⁿ = 0.7576

p_(C.sensitive_{) = Q} ^^ _p ^.insensitive) _{= Q} g_{3 1}1

Rule 31

Gene 1: SID W 510395 Ribosomal protein S16 [5':AA053701 3':AA053681] Gene 2: SID W 345420 Homo sapiens YAC clone 136A2 unknown mRNA 3'untranslated region [5*:W76024 3':W72468] Drag: Clomesone Parameters: μ_k ^sen = -0.4557, μι^sen = 0.7165, σ_k ^sen = 0.2618, σι^sen = 0.4934, _Pk ^en = -0.4265 _. ^^^'^0 = 0.05367 p_(c.sensitiv_{e) = 0> 1917j} p^insensitive) _{= Q 8()83}

Rule 32 Gene 1 : ESTs Weakly similar to GAR22 protein [H.sapiens] Chr. [51904 (E) 5':H24408 3':H22555]

Gene 2: SID 147338 ESTs [5': 3':H01302] Drag: Clomesone Parameters: μ_k ^sen = 0.3048, μι^sen = 0.1604, σ_k ^sen = 0.4287, σι^sen = 0.37, _Pk)ι^sen = -0.7076

P(Ci^sensitive) = 0.1917, P(c ^sensitive) = 0.8083

Rule 33 Gene 1 : MSN Moesin Chr.X [486864 (IW) 5':AA043008 3':AA042882]

Gene 2: Human mRNA for reticulocalbin complete cds Chr.l 1 [485209 (IW) 5':AA039292 3':AA039334] Drag: Clomesone Parameters: μ_k ^sen = 0.6791, μι^sen = 0.4913, σ_k ^sen = 0.4486, σfⁿ = 0.4435, p_Ki ^sen = 0.8962

P(Ci^{sensi ive}) = 0.1917, = 0.8083

Rule 34 Gene 1 : Homo sapiens gamma2-adaptin (G2AD) mRNA complete cds Chr.14 [415647 (IW) 5':W78996 3':W80537]

Gene 2: ESTs Chr.6 [146640 (I) 5':R80056 3':R79962] Drug: Fluorouracil (5FU) Parameters: μ_k ^sen = 0.3802, μι^sen = 0.1649, σ_k ^sen = 0.419, σι^sen = 0.7902, _Pk ^en = 0.9422

₁^(8^0 = 0.04435 P( ^sensitive) = 0.1628, p(Q^insensitive) = 0.8372 Rule 35

Gene 1: SID W 415811 ESTs [5':W84831 3':W84784]

Gene 2: H.sapiens mRNA for Gal-beta(l-3/l-4)GlcNAc alpha-2.3-sialyltransferase Chr.l 1 [324181 (IW) 5':W47425 3':W47395] Drag: Fluorouracil (5FU) Parameters: μ_k ^sen = -0.16, μι^sen = -0.3532, σ_k ^sen = 0.2818, σι^sen = 0.2383, _Pk;1 ^sen = 0.2669 ^(^ = 0.0438 p_(C.^sensitiv _{e) =} o.i628, ^cr ^ ) = 0.8372

Rule 36

Gene 1 : SID 289361 ESTs [5':N99589 3':N92652] Gene 2: EST Chr.l [137318 (I) 5': 3':R36703] Drag: Fluorouracil (5FU) Parameters: μ_k ^sen = 0.03614, μfⁿ = -0.3758, σ_k ^sen = 0.186, σι^sen = 0.4475, _Pk)fⁿ = -0.1074 ^_,1^0 = 0.06362 P(Cι^sensitive) = 0.1628, p(c_i ^insensitive) = 0.8372

Rule 37

Gene 1 : LAMA3 Laminin alpha 3 (nicein (150kD) kalinin (165kD) BM600 (150kD) epilegrin) Chr.18 [362059 (TRW) 5':AA001431 3':AA001432]

Gene 2: Prostacyclin-stimulating factor [human cultured diploid fibroblast cells mRNA 1124 nt] Chr.4 [488721 (IW) 5':AA046078 3':AA046026] Drug: Cytarabine (araC) Parameters: μ_k ^sen = -0.3545, μι^sen = -0.4411, σ_k ^seπ = 0.7334, σfⁿ = 0.5863, _Pk,ι^sen = 0.8148

P(Cι^sensitive) = 0.2661, p(c_i ^insensitive) = 0.7339

Rule 38

Gene 1: ESTs Chr.14 [244047 (I) 5':N45439 3':N38807]

Gene 2: SID 307717 Homo sapiens KIAA0430 mRNA complete cds [5': 3':N92942] Drug: Cyclocytidine Parameters: μ_k ^sen = 0.536, μι^sen = 0.004825, σ_k ^sen = 0.4307, σι^sen = 0.232, _Pk ^en = 0.1655 . ^₁^0 = 0.03336 p(_C.sensitiv_{e) = 0>25335} p_(C.insensitiv_{e) = Q 46η}

Rule 3399 Gene ! 1: ESTs Chr.l [31905 (I) 5':R17893 3*:R43139]

Gene ! 22:: SSIIDD 3 : 07717 Homo sapiens KIAA0430 mRNA complete cds [5': 3':N92942] Drag: Cyclocytidine Parameters: μ_k ^sen = 0.1955, μι^sen = 0.004825, σ_k ^sen = 0.7301, σι^sen = 0.232, _Pk,ι^sen = 0.685 iUkα s'tog¹!) = 0.03972

P(Cι^sensitive) = 0.2533, p(c_i ^insensitive) ^■= 0.7467

Rule 40

Gene 1 : SID W 193562 Homo sapiens nuclear autoantigen GS2NA mRNA complete cds [5':H47460 3':H47370]

Gene 2: SID 307717 Homo sapiens KIAA0430 mRNA complete cds [5': 3':N92942] Drug: Cyclocytidine Parameters: μ_k ^sen = 0.3942, μι^sen = 0.004825, σ_k ^sen = 0.7788, σι^sen = 0.232, _Pk ^en = 0.5508

P(Cι^sensitive) = 0.2533, p(c_i ^insensitive) = 0.7467

Rule 41

Gene 1: ALDOC Aldolase C fructose-bisphosphate Chr.17 [229961 (IW) 5':H67774 3*:H67775]

Gene 2: SID 470499 Human mRNA for KIAA0249 gene complete cds [5':AA031742 3':AA031651]

Drug: Anthrapyrazole-derivative Parameters: μk ' = -0.2373, μι^sen = 0.4104, σ_k ^sen = 0.3786, σι^sen = 0.5297, _k ^en = -0.7901

P(Cι^sensitive) = 0.2006, p( ^{insensi ive}) = 0.7994

Rule 42 Gene 1 : SID 471855 Lumican [5': 3':AA035657] Gene 2: Thioredoxin Reductase mRNA-log Drag: Menogaril Parameters: μ_k ^sen = -0.5946, μι^sen = 0.4827, σ ^sen = 0.3149, σι^sen = 0.4498, _Pk,ι^sen= 0.8286 _iU_k4(gⁱ _k,gⁱ0 = 0.03953

P(Cι^sensitive) = 0.1944, p(c ^sensitive) = 0.8056

Rule 43

Gene 1: ESTsSID 327435 [5':W32467 3*:W19830] Gene 2: PROBABLE TRANS- 1.2-DIHYDROBENZENE-l .2-DIOL DEHYDROGENASESID 211995 [5':H75805 3':H68500] Drug: Hydroxyurea Parameters: μ_k ^sen = -0.3875, μι^sen = -0.05828, σ_k ^sen = 0.3831, σι^sen = 0.3997, _Pk ^en = 0.8287 iU- (gⁱ _k,g^,"ι) = 0.05168 .

P( ^sensitive) = 0.1483, p( ^insensitive) = 0.8517

Rule 44

Gene 1 : ESTs Chr.1 [62232 (IR) 5':T40284 3*:T41149] Gene 2: SID W 488455 Cathepsin D (lysosomal aspartyl protease) [5':AA047512 3*:AA047455] Drag: CPT,10-OH Parameters: μ_k ^sen = 0.07749, μι^sen = 0.249, σ_k ^sen = 0.7379, σι^sen = 0.4558, _Pk,ι^sen = 0.6965

P(Cι^sensitive) = 0.1856, p( ^insensitive) = 0.8144

Rule 45 Gene 1: SID W 417320 Plasminogen activator tissue type (t-PA) [5':W88922 3':W89129]

Gene 2: Homo sapiens Cyr61 mRNA complete cds Chr.l [486700 (DIW) 5':AA044451 3':AA044574] 5 Drag: CPT,10-OH Parameters: μ_k ^sen = 0.614, μfⁿ = 0.6231, σ_k ^sen = 0.4658, σfⁿ = 0.6676, _Pk ^en = -0.7235 iUk ώtogO ^ 0.05368 _p(c.sensitiv_{e) = Q 18505 p(c}.insensitive_{) = Q 44}

R Kuullee 4460

Gene 1: ESTs Chr.6 [471083 (IW) 5':AA034335 3':AA033710]

Gene 2: SID W 488148 H.sapiens mRNA for 3'UTR of unknown protein [5':AA057239

3':AA058703]

15 Drag: CPT

Parameters: μ_k ^sen = -0.2213, μι^sen = 0.8224, σ_k ^sen = 0.6777, σι^sen = 0.5588, _Pk,ι^sen = 0.62

^₁^0 = 0.04033

P(C;^sensitive) = 0.2594, P(C_i ^insensitive) = 0.7406 on

Gen Lβe 1 : *Homo sapiens lysosomal neuraminidase precursor mRNA complete cds SID

W w ^487887 Hexabrachion (tenascin C cytotactin) [5':AA046543 3*:AA045473] Gene 2: ESTs Weakly similar to ! ! ! ! ALU SUBFAMILY J WARNING ENTRY ! ! ! ! 25 [H.sapiens] Chr. [21955 (I) 5':T66210 3':T66144] Drug: CPT Parameters: μ_k ^sen = 0.3188, μι^sen = 0.5775, σ_k ^sen = 0.7221, σι^sen = 0.5522, _Pk>1 ^sen = -0.8619

30 P(Cι^sensitive) = 0.2594, p(C_i ^insensitive) = 0.7406

Rule 48

Gene 1: SID W 365476 Protein S (alpha) [5":AA009419 3':AA009723] Gene 2: SID W 488148 H.sapiens mRNA for 3'UTR of unknown protein [5':AA057239 3':AA058703] Drag: CPT Parameters: μ_k ^sen = -0.03662, μι^sen = 0.8224, σ_k ^sen = 0.6534, σι^sen = 0.5588, _k,ι^sen = -0.6764

^(^^' = 0.06166

P(Cι^sensitive) = 0.2594, p(c_i ^insensitive) = 0.7406

Rule 49 Gene 1 : SID 469530 H.sapiens mRNA for ragA protein [5': 3': AA026944]

Gene 2: Homo sapiens clone 24477 mRNA sequence Chr.18 [33059 (IEW) 5':R19498 3*:R43846] Drag: CPT Parameters: μ_k ^sen = 0.459, μι^sen = -0.2041, σ_k ^sen = 0.5722, σι^sen = 0.6591, _Pk ^en = -0.8312

₁^,₁^0 = 0.04669 P(Cι^sensitive) = 0.2594, p(c_i ^insensitive) = 0.7406

Rule 50 Gene 1 : SID W 469299 ETS-RELATED PROTEIN ERM [5':AA026205 3':AA026121] Gene 2: SID W 415693 Homo sapiens mRNA for phosphatidylinositol 4-kinase complete cds [5':W78879 3':W84724] Drug: CPT Parameters: μ_k ^sen = -0.0352, μι^sen = 0.664, σ_k ^sen = 0.5333, σι^sen = 0.6375, _Pk ^en = -0.8029

₁^₁^0 = 0.0497 P(Cι^sensitive) = 0.2594, p(c ^seπsitive) = 0.7406

Rule 51 Gene 1 : SID W 488148 H.sapiens mRNA for 3'UTR of unknown protein [5*: AA057239 3':AA058703]

Gene 2: HLA-DRB5 Major histocompatibility complex class II DR beta 5 Chr.6 [321230 (IEW) 5':W52918 3':AA037380] Drug: CPT Parameters: μ_k ^sen = 0.8224, μι^sen = -0.07462, σ_k ^sen = 0.5588, σι^sen = 0.7144, _k ^en = -0.8079 iU_k,,(g^, _k,g^J0 = 0.05766 P(Q^sensMvβ) = 0.2594, p( ^imensittve) = 0.7406

Rule 52

Gene 1: ESTs Chr.5 [322749 (I) 5': 3':W15473]

Gene 2: SID 469530 H.sapiens mRNA for ragA protein [5': 3':AA026944] Drag: CPT Parameters: μ_k ^sen = -0.02124, μι^sen = 0.459, σ_k ^sen = 0.5919, σι^sen = 0.5722, _Pk,ι^sen = -0.8235 iU_kιι(g^j _k,g^l0 = 0.05028 P(Cι^sensitive) = 0.2594, p(c_i ^insensitive) -= 0.7406

Rule 53

Gene 1: SID W 159512 Integrin alpha 6 [5':H16046 3':H15934] Gene 2: SID 301276 ESTs Highly similar to VALYL-TRNA SYNTHETASE [Fugu rabripes] [5':W07581 3':N80811] Drag: CPT Parameters: μ_k ^sen = 0.7291, μι^sen = 0.6257, σ_k ^sen = 0.6557, σι^sen = 0.6193, _Pk ^en = -0.1667 iU_k,ι( ^j _k,g^j ₁) -= 0.05021 P(Ci^sensitive) = 0.2594, (C_i ^ioa∞άaw) = 0.7406

R κuulιee 504-

Gene 1 : SID W 125268 H.sapiens mRNA for human giant larvae homolog [5':R05862 3':R05776]

Gene 2: G6PD Glucose-6-ρhosρhate dehydrogenase Chr.X [430251 (IW) 5':AA010317 3':AA010382]

Drug: Chlorambucil Parameters: μ_k ^sen = -0.4569, μ,^sen = -0.2982, σ_k ^sen = 0.4595, σι^sen = 0.2945, _Pk,ι^sen = -0.1414 iU_k,i(g^,' _k,g^J'0 = 0.06214

P(Q^sensitive) = 0.2206, p(C_i ^insensitive) = 0.7794

Rule 55 Gene 1 : SID W 510534 MAJOR GASTROINTESTINAL TUMOR-ASSOCIATED PROTEIN GA733-2 PRECURSOR [5':AA055858 3':AA055808] Gene 2: G6PD Glucose-6-phosphate dehydrogenase Chr.X [430251 (IW) 5':AA010317 3':AA010382] Drag: Chlorambucil Parameters: μ_k ^sen = -0.7249, μι^sen = -0.2982, σ_k ^sen = 0.5634, σι^sen = 0.2945, _Pk;fⁿ = -0.3986

P(Cj^sensitive) = 0.2206, p(c ^sensitive) = 0.7794

Rule 56

Gene 1: SID 29828 ESTs [5':R16390 3':R42331]

Gene 2: SID W 485645 KERATIN TYPE II CYTOSKELETAL 7 [5*:AA039817 3':AA041344]

Drag: 5-Hydroxypicolinaldehyde-thiose Parameters: μ_k ^sen = -0.1536, μfⁿ = 0.8712, σ_k ^sen = 0.5974, σι^sen = 0.6735, _k ^en = 0.6716 iU_k,,(g^j _k,gⁱ0 = 0.03954 P(Cι^sensitive) = 0.1789, ?(cr^eΩsiήwβ) = 0.8211

Rule 57

Gene 1: SID 381780 ESTs [5':AA059257 3':AA059223] Gene 2: SID 130482 ESTs [5':R21876 3':R21877] Drag: Paclitaxel — Taxol Parameters: μ_k ^sen = 0.1618, μι^sen = -0.9271, σ_k ^sen = 0.1828, σι^sen = 0.3413, _Pk,ι^sen = -0.3935

₁^₁^0 = 0.05375 P(Cι^sensitive) = 0.1622, p(Q^insensitive) = 0.8378 Rule 58

Gene 1: SID 381780 ESTs [5':AA059257 3':AA059223]

Gene 2: SID 512355 ESTs Highly similar to SRC SUBSTRATE P80/85 PROTEINS [Gallus gallus] [5':AA059424 3':AA057835] Drug: Paclitaxel — Taxol Parameters: μ_k ^sen = 0.1618, μι^sen = -0.8354, σ_k ^sen = 0.1828, σι^sen = 0.4935, _Pk,ι^sen = -0.09957 ₁^_,1(^0 = .06437 P(Cι^sensitive) = 0.1622, p(c_i ^insensitlve) = 0.8378

Rule 59

Gene 1 : *Paired basic amino acid cleaving enzyme (furin membrane associated receptor protein) SID W 114116 Syndecan 2 (heparan sulfate proteoglycan 1 cell surface- associated fibroglycan) [5':T79562 3':T79471] Gene 2: SID 240167 ESTs [5':H79634 3':H79635] Drag: Pyrazoloacridine Parameters: μ_k ^sen = -0.6405, μι^sen = 0.3087, σ_k ^sen = 0.5377, σι^sen = 0.4283, _Pk ^en = 0.7929 ₁^₁(^0 = 0.05053 P(Q^sensitive) = 0.1811, p( ^{inseιlsitive}) = 0.8189

Linear Discriminant Analysis - 1 -dimensional (LDA ID)

This method computes a Bayesian conditional probability P(^J i s '^sensitive I ^{[ S} 2^kJ

that a cell line ^ is sensitive to drag ^z , given the gene k abundance %^k in cell line ^7" .

The probability is computed using the following equation:

where ^■ps -isensitives ^■ -^' -' ^"prior probability ofthe sensitive set

prior probability ofthe insensitive

of abundance value gl from the gaussian density fitted to the histogram ofthe gene k abundances over the sensitive cell lines when subjected to drug i .

(.sensitive , js _ _p s[ -μf Ϋ ' K°7* ?

where μ_k ⁿ = mean of gene k abundances in the sensitive cell lines avg ^k = sensitive\insensitive class-weighted average standard deviation of gene k abundances in the sensitive cell lines

_iG_k ^nsem"^lve (g{) = probability of abundance value g_k' from the gaussian density fitted to the histogram ofthe gene k abundances over the insensitive cell lines when subjected to drag i .

' Qinsensitive , js _{= c}-(g/-/«f")² 2(σ_t"»)

' * ^^{k )} _σ^J2 where

μ_k" ^e" = mean of gene k abundances in the insensitive cell lines

Sample parameters for the LDA ID analysis on the NCI60 Dataset are set out below:

Rule 1

Gene: SID W 470947 Human scaffold protein Pbpl mRNA complete cds [5':AA032174 3':AA032175]

Drag: Inosine-glycodialdehyde

Parameters: μ_k ^sen = -0.8115 μ_k ^insen = 0.2001 σ_k ^av = 0.9394

P(Q^sensitive) = 0.1978, p(c_i ^insensitive) = 0.8022

Rule 2 Gene: Human mRNA for reticulocalbin complete cds Chr.11 [485209 (IW) 5':AA039292 3':AA039334] Drag: Inosine-glycodialdehyde Parameters: μ_k ^sen = -0.7618 μ_k ^insen = 0.1878 σ_k ^avg = 0.9598 P(Cι^sensitive) = 0.1978, p(c_i ^insensitive) = 0.8022

Rule 3 Gene: Homo sapiens cyclin-dependent kinase inhibitor (CDKN2C) mRNA complete cds Chr. [291057 (RW) 5':W00390 3':N72115] Drag: L-Alanosine Parameters: μ_k ^sen = -0.8435 μ_k ^insen = 0.25 σ_k ^avg = 0.8772 P(Cι^sensitive) = 0.2283, p(Q^insensitive) = 0.7717

Rule 4 Gene: SID W 254085 ESTs Moderately similar to synaptonemal complex protein [M.musculus] [5':N71532 3':N22165] Drag: Baker's-soluble-antifoliate Parameters: μ_k ^sea = 0.7847 μ ^to^∞ = -0.2423 σ_k ^avg = 0.8539 p(_C.sensitive_{) = Q2}S6\, p(C;^{inSensi ive}) = 0.7639

Rule 5

Gene: M-PHASE INDUCER PHOSPHATASE 2 Chr.20 [179373 (EW) 5*:H50437 3':H50438] Drag: 5-6-Dihydro-5-azacytidine Parameters: μ_k ^sen = -0.9251 μ_k ^insen = 0.2324 σ_k ^avg = 0.8567 p(_C.sensitiv_{e) = 0 201 λ} ^ p^.insensitive) _{= Q ?989}

Rule 6

Gene: THY-1 MEMBRANE GLYCOPROTEIN PRECURSOR Chr.l 1 [183950 (E) 5':H30297 3*:H28104] Drag: Mitozolamide Parameters: μ_k ^sen = 1.073 μ_k ^insen = -0.2694 σ_k ^avg = 0.8153 p₍ s^βnsitive_{) = 0ι2}006, p(C_i ^insensitive) = 0.7994

Rule 7

Gene: PTN Pleiofrophin (heparin binding growth factor 8 neurite growth-promoting factor 1) Chr.7 [488801 (IW) 5':AA045053 3':AA045054] Drag: Mitozolamide Parameters: μ_k ^sen = 1.019 _μkinsen ₌ .Q.2557 σ_k ^avg = 0.8554

P(Cι^sensitive) = 0.2006, P(C_i ^insensitive) = 0.7994

Rule 8

Gene: SID W 380674 ESTs [5':AA053720 3*:AA053711] Drag: Mitozolamide Parameters: μ_k ^sen = 1.093 μ_k ^insen = -0.2739 σ_k ^avg = 0.8441 P(Cι^sensitive) = 0.2006, p(c_i ^insensitive) -= 0.7994

Rule 9 Gene: Glutathoine S-Tranferase Pi-log Drag: Mitozolamide Parameters: μ_k ^sen = -0.917 μ_k ^insen = 0.2307 σ_k ^avg = 0.8411

P(Ci^sensitive) = 0.2006, p(c ^sensitive) = 0.7994

Rule 10

Gene: SID W 242844 ESTs Moderately similar to ! ! ! ! ALU SUBFAMILY J WARNING ENTRY ! !!! [H.sapiens] [5':H94138 3*:H94064] Drag: Mitozolamide Parameters: μ_k ^sen = -1.008 μ_k ^insen = 0.2536 σ_k ^av = 0.8681

P(Cι^sensitive) = 0.2006, f c ^eDsitive) = 0.7994 Rule 11

Gene: *Hs.648 Cut (Drosophila)-like 1 (CCAAT displacement protein) SID W 26677 ESTs [5':R13994 3':R39117] Drag: Mitozolamide Parameters: μ_k ^sen = 0.8138 μ_k ^insen = .0.2039 σ_k ^av = 0.9103

P(C;^sensitive) = 0.2006, p(C_i ^insensitive) = 0.7994

Rule 12

Gene: SID W 488387 Exostoses (multiple) 2 [5':AA046786 3':AA046656] Drug: Cyclodisone Parameters: μ_k ^sen = 1.043 μ_k ^insen = - 0.2128 σ_k ^avg = 0.8985

P(C;^sensitive) = 0.1689, p(Q^insensitive) = 0.8311

Rule 13

Gene: THY-1 MEMBRANE GLYCOPROTEIN PRECURSOR Chr.11 [183950 (E) 5':H30297 3':H28104] Drag: Cyclodisone Parameters: μ_k ^sen = 1.135 μ_k ^insen = -0.2308 σ_k ^avg = 0.8251

P(Cι^sensitive) = 0.1689, P Q™*⁷⁶) = 0.8311

Rule 14

Gene: SID W 487535 Human mRNA for KIAA0080 gene partial cds [5':AA043528 3':AA043529] Drag: Clomesone Parameters: μ_k ^sen = 1.184 ^' μ_k ^insen = -0.2817 σ_k ^avg = 0.829

P( ^sensitive) - 0.1917, p(c ^sensitive) = 0.8083

Rule 15

Gene: PTN Pleiofrophin (heparin binding growth factor 8 neurite growth-promoting factor 1) Chr.7 [488801 (IW) 5':AA045053 3':AA045054] Drag: Clomesone Parameters: μ_k ^sen = 1.14 μ_k ^insen = - 0.2703 σ_k ^avg = 0.8309

P( ^sensitive) = 0.1917, p(Q^ksensitive) = 0.8083

Rule 16

Gene: THY-1 MEMBRANE GLYCOPROTEIN PRECURSOR Chr.11 [183950 (E) 5':H30297 3':H28104] Drag: Clomesone Parameters: μ_k ^sen = 1.157 μ_k ^insen = -0.2746 σ_k ^avg = 0.8226

P( ^sensitive) = 0.1917, P( ^iπsensitive) = 0.8083

Rule 17

Gene: SID W 242844 ESTs Moderately similar to ! ! ! ! ALU SUBFAMILY J WARNING ENTRY !!!! [H.sapiens] [5':H94138 3':H94064] Drag: Clomesone Parameters: μ_k ^sen = -1.079 μk^insβn = 0.2564 σ_k ^av = 0.8587

P(Cι^sensitive) = 0.1917, P( ^insensitive) = 0.8083

Rule 18

Gene: SID W 487535 Human mRNA for KIAA0080 gene partial cds [5':AA043528 3':AA043529] Drug: PCNU Parameters: μ_k ^sen = 1.081 μ_k ^insen = _:0.2435 σ_k ^avg = 0.8791

P( ^sensitive) = 0.1833, p( ^insensitive) = 0.8167

Rule 19

Gene: SID W 242844 ESTs Moderately similar to ! ! ! ! ALU SUBFAMILY J WARNING ENTRY !!!! [H.sapiens] [5':H94138 3':H94064] Drug: PCNU Parameters: μ_k ^sen = -1.078 μ_k ^insen = 0.2427 σ_k ^avg = 0.8755

P(Q^sensitive) = 0.1833, p( ^insensitive) = 0.8167

Rule 20

Gene: PTN Pleiofrophin (heparin binding growth factor 8 neurite growth-promoting factor 1) Chr.7 [488801 (IW) 5*:AA045053 3':AA045054] Drag: PCNU Parameters: μ_k ^sen = 1.115 μ_k ^insen = -0.2502 σ_k ^av = 0.8538

P( ^sensitive) = 0.1833, p(Q^insensitive) = 0.8167

Rule 21 Gene: Human thymosin beta-4 mRNA complete cds Chr.20 [305890 (IW) 5':W19923 3':N91268]

Drug: Cytarabine (araC) Parameters: μ_k ^sen = -0.7694 μ_k ^insen = 0.2788 σ_k ^av = 0.8663 P( ^sensitive) = 0.2661, p( ^insensitive) = 0.7339

Rule 22 Gene: SID W 291620 Restin (Reed-Steinberg cell-expressed intermediate filament- associated protein) [5':W03421 3':N67817] Drug: Porfiromycin Parameters: μ_k ^sen = 0.9491 μ_k ^insen = -0.2431 σ_k ^avg = 0.8965 P(Cι^sensitive) = 0.2039, P(c ^sensitive) = 0.7961

Rule 23 Gene: Human extracellular protein (Sl-5) mRNA complete cds Chr.2 [485875 (EW) 5':AA040442 3':AA040443] Drag: Oxanthrazole (piroxantrone) Parameters: μ_k ^sen = 1.155 μ_k ^insen = -0.2805 σ_k ^av = 0.7962 P(C;^sensitive) = 0.1956, p(c_i ^insensitive) = 0.8044 Rule 24

Gene: SID W 299539 Human fibroblast growth factor homologous factor 1 (FHF-1) mRNA complete cds [5':W05845 3':N71102] Drag: Oxanthrazole (piroxantrone) Parameters: μ_k ^sen = 0.9238 _μkinsen ₌ _0_ι2254 σ_k ^avg = 0.862 p(_C.sensitive_{) = Q} j g^ p^.insensitive) _{= Q 8Q44}

Rule 25

Gene: SID W 488148 H.sapiens mRNA for 3'UTR of unknown protein [5':AA057239 3':AA058703] Drag: Oxanthrazole (piroxantrone) Parameters: μ_k ^sen = 0.8896 μ_k ^insen = -0.2163 σ_k ^avg = 0.8858 p_(C._Sensitive_{) = Q} ^^ _p(c. insensitive) _{= QM}

Rule 26

Gene: Human extracellular protein (Sl-5) mRNA complete cds Chr.2 [485875 (EW) 5':AA040442 3':AA040443] Drag: Anthrapyrazole-derivative Parameters: μ_k ^sen = 1.016 μ_k ^insen = -0.2548 σ_k ^av = 0.8692 p(_C.sensi^tive) ₌ Q.2006, p(c ^sensitive) = 0.7994

Rule 27 Gene: SID W 380674 ESTs [5':AA053720 3':AA053711]

Drag: Anthrapyrazole-derivative

Parameters: μ_k ^sen = 0.9038 μ_k ^insen = -0.2265 σ_k ^avg = 0.8998 p_(c.sensitive) ₌ Q^QQβ, p(C_i ^inSensitive) = 0.7994

Rule 28 Gene: ESTs Chr.2 [365120 (IW) 5':AA025204 3':AA025124] Drag: Anthrapyrazole-derivative Parameters: μ_k ^sen = 0.9014 μ_k ^insen = -0.2264 σ_k ^avg = 0.9007

P(C_; ^sensitive) = 0.2006, p(c_i ^insensitive) = 0.7994

Rule 29

Gene: SID 229535 [5*:H66594 3':H66595] Drag: Teniposide Parameters: μ_k ^sen = -0.9209 μ_k ^insen = 0.2154 σ_k ^avg = 0.9114 p_(C.sensitive) _{= QA m>} p_(C.insensitiv_e) _{= QM Q6}

Rule 30

Gene: ESTs Chr.2 [149542 (DW) 5':H00283 3':H00284] Drag: Daunorabicin Parameters: μ_k ^sen = -1.052 μ_k ^insen = 0.2324 σ_k ^avg = 0.8508 _p(c.sensi^tive) _{= 0}.1811, p( ^insensitive) = 0.8189

Rule 31 Gene: SID W 510030 ESTs Weakly similar to N-methyl-D-aspartate receptor glutamate- binding chain [R.norvegicus] [5':AA053050 3':AA053392] Drag: Daunorabicin Parameters: μ_k ^sen = -1.088 μ_k ^insen = 0.2401 σ_k ^avg = 0.8526 _p(c.sensitive) = 0.1811, p(c ^sensitive) = 0.8189

Rule 32 Gene: SID 260288 ESTs [5':H97716 3':H96798] Drag: Daunorabicin Parameters: μ_k ^sen = -0.9929 μ_k ^insen = 0.2192 σ_k ^avg = 0.9063 _p(c.sensi^tive) = Q.181 1 , p(Q ^sensitive) = 0.8189

Rule 33

Gene: AKl Adenylate kinase 1 Chr.9 [488381 (IW) 5':AA046783 3*:AA046653] Drug: Daunorabicin Parameters: μ_k ^sen = -0.9847 μ_k ^insen = 0.2169 σ_k ^avg = 0.8611 p_(C.sensi^tive) ₌ Q.I 811, p(Q^insensitive) *= 0.8189

Rule 34 Gene: Homo sapiens T245 protein (T245) mRNA complete cds Chr.X [343063 (IW) 5':W67989 3':W68001] Drug: Daunorabicin Parameters: μ_k ^sen = -1.061 μ_k ^insen = 0.234 σ_k ^avg = 0.8647 _p(c._Sensitive) ₌ 0.1811, p(_Ci™t-v_{e) = 0>8189}

Rule 35

Gene: *Prothymosin alpha SID W 271976 AMINO AC YLASE-1 [5':N44687 3':N35315] Drug: Daunorabicin Parameters: μ_k ^sen = -1.032 μ_k ^insen = 0.2284 σ_k ^av = 0.858 _p(c.sensitive) _{= Q} -^^ _p(c. insensitive) _{= Q} g^

Rule 36 Gene: SID W 345683 ESTs Highly similar to INTEGRAL MEMBRANE

GLYCOPROTEIN GP210 PRECURSOR [Rattus norvegicus] [5':W76432 3':W72039] Drug: Daunorabicin Parameters: μ_k ^sen = 0.918 μ_k ^insen = -0.2022 σ_k ^avg = 0.8758 p_(c.sensitive) ₌ Q ₁₈₁ ^ p_(c.insensitive) _{= Q 81 89}

Rule 37 Gene: Homo sapiens clone 24477 mRNA sequence Chr.l 8 [33059 (IEW) 5':R19498 3':R43846] Drug: Daunorabicin Parameters: μ_k ^sen = -0.966 μ_k*^∞> = 0.2126 σ_k ^avg = 0.8952 p_(C.sensitive) ₌ Q.1811 , p( ^insensitive) = 0.8189

Rule 38

Gene: SID 43609 ESTs [5':H06454 3':H06184] Drag: Amsacrine Parameters: μ_k ^sen = 0.9136 μ_k ^insen = -0.2581 σ_k ^avg = 0.8733

P(Ci^sensitive) = 0.22, P(C_i ^insensitive) = 0.78

Rule 39

Gene: GAMMA-INTERFERON-INDUCIBLE PROTEIN IP-30 PRECURSOR Chr.19 [310021 (1) 5': 3':N99151] Drag: CPT,10-OH Parameters: μ_k ^sen = -0.9086 μ_k ^insen = 0.2078 σ_k ^avg = 0.8915 p_(c.sensitive) ₌ ^g^ _p ^.insensitive) _{= QM44} ,

Rule 40

Gene: SID W 346587 Homo sapiens quiescin (Q6) mRNA complete cds [5':W79188 3*:W74434] Drug: CPT,10-OH Parameters: μ_k ^sen = 1.001 μ_k ^insen = -0.2285 σ_k ^avg = 0.8549 _p(c.sensitive) _{= Q} j ^ _p(c.insensitive) _{= Q 8 H4}

Rule 41 Gene: SID 39144 ESTs Weakly similar to Rep-8 [H.sapiens] [5':R51769 3':R51770] Drug: CPT,20-ester (S) Parameters: ■ μ_k ^sen = -0.8367 μ_k ^insen = 0.2555 σ_k ^avg = 0.8798 _p(c.sensitive) ₌ Q^^ p_(C.insensitive) _{= Q 656}

Rule 42

Gene: SID W 358526 ESTs [5':W96039 3*:W94821] Drag: CPT, 14-C1 (S) Parameters: μ_k ^sen = -0.8436 μ_k ^insen = 0.2136 σ_k ^avg = 0.9027 p_(C.^sensitive) _{= 0<} 022, p(c ^sensitive) = 0.7978

Rule 43

Gene: GAMMA-INTERFERON-INDUCIBLE PROTEIN IP-30 PRECURSOR Chr.19 [310021 (I) 5': 3':N99151] Drag: CPT,20-acetate Parameters: μ_k ^sen = -0.8754 μ_k ^insen = 0.1973 ■ σ_k ^avg = 0.8929 _P(-_C.sensitive) _{= 0 t 833j} p^.insensitive) _{= g l 67}

Rule 44 Gene: SID 512355 ESTs Highly similar to SRC SUBSTRATE P80/85 PROTEINS [Gallus gallus] [5':AA059424 3*:AA057835] Drag: CPT Parameters: μ_k ^sen = 0.8614 μ_k ^insen -= -0.3016 σ_k ^avg = 0.8698

P(Q^sensitive) = 0.2594, p(Q^insensitive) = 0.7406

Rule 45

Gene: SID W 488148 H.sapiens mRNA for 3*UTR of unknown protein [5':AA057239 3':AA058703] Drag: CPT Parameters: μ_k ^sen = 0.8224 μ_k ^insen = - 0.2881 σ_k ^avg = 0.8739 p_(c.sensitive) _{= Q} ^^ p_(C. insensitive) _{= Q}

Rule 46

Gene: ESTs Chr.19 [485804 (EW) 5':AA040350 3':AA040351] Drag: CPT,20-ester (S) Parameters: μ_k ^sen = -0.7505 μ_k ^insen = 0.2562 σ_k ^avg = 0.8843 _p(c.sensitive) ₌ Q ^ _p(Cjinsensitive) _{= Q 4S}

Rule 47 Gene: SID W 358526 ESTs [5':W96039 3':W94821] Drug: CPT,l l-formyl (RS) Parameters: μ_k ^sen = -1.055 ^• μ_k ^insen = 0.2536 σ_k ^avg = 0.8569 _p(c.sensitive) ₌ Q.1939, p(Qinsensitive) _{= Q 806}1

Rule 48

Gene: SID W 135118 GATA-binding protein 3 [5':R31441 3':R31442] Drug: CPT, 11 -formyl (RS) Parameters: μ_k ^sen = 0.9817 μ_k ^insen = - 0.2359 σ_k ^avg = 0.9021 _p(c.sensitive) _{= Q m9>} p^ensitive) _{= Q l}

Rule 49

Gene: ESTs Chr.16 [154654 (RW) 5':R55184 3':R55185] Drug: CPT, 11 -formyl (RS) Parameters: μ_k ^sen = 0.874 μ_k ^insen = -0.2102 σ_k ^avg = 0.9112 P(Cι^sensitive) = 0.1939, P(C_i ^insensitive) = 0.8061

Rule 50 Gene: SID 43609 ESTs [5':H06454 3':H06184] Drag: Mechlorethamine Parameters: μ_k ^sen = 1.042 μ_k ^insen = -0.2493 σ_k ^avg = 0.8728

P(Cι^sensitive) = 0.1928, p(C_i ^insensitive) = 0.8072 Rule 51

Gene: SID W 133851 ESTs [5':R28233 3':R27977] Drag: Triethylenemelamine Parameters: μ_k ^sen = -0.7551 μ_k ^insen = 0.2248 σ_k ^avg = 0.9176 _p(c.^sensitive) ₌ Q.2294, p(c_i ^{tosenβitivβ}) = 0.7706

Rule 52

Gene: SID W 133851 ESTs [5':R28233 3*:R27977]

Drag: Chlorambucil

Parameters: μ_k ^sen = -0.8278 μ_k ^insen = 0.2342 σ_k ^av = 0.8901 _p(c.sensitive) _{= 0}.₂206, P(C_i ^insensitive) = 0.7794

Rule 53 Gene: Human mRNA for KIAA0382 gene partial cds Chr.11 [486712 (IEW) 5':AA043173 3':AA043174] Drag: Chlorambucil Parameters: μ_k ^sen = -0.8832 μ_k ^insen = 0.2497 σ_k ^av = 0.8826 p_(c.sensitive) _{= Q 22}06, p(Q^insensitive) = 0.7794

Rule 5.4 Gene: CDH2 Cadherin 2 N-cadherin (neuronal) Chr. [325182 (DIRW) 5*:W48793 3':W49619] Drag: Geldanamycin Parameters: μ_k ^sen = -0.8842 μ_k ^insen = 0.225 σ_k ^avg = 0.8839 _P(-_C.^seⁿs^itive) _{= 0 20}33, p^ ⁶¹¹³^⁶) = 0.7967

Rule 55

Gene: Human nicotinamide nucleotide franshydrogenase mRNA nuclear gene encoding mitochondrial protein Chr. [287568 (I) 5': 3':N62116] Drug: Morpholino-adriamycin Parameters: μ_k ^sen = -1.072 μ_k ^insen = 0.2139 σ_k ^av = 0.8933 positive) _{= Q j}^ _p ^.insensitive) ₌ ^335

Rule 56

Gene: H.sapiens mRNA for TRAMP protein Chr.8 [149355 (IEW) 5':H01598 3':H01495] Drag: Amonafide Parameters: μ_k ^sen = 1.095 μ_k ^insen = -0.2498 σ_k ^avg = 0.8687 p_(C.sensitive) _{= Q} ^^ _p ^.insensitive) _{= Q} ^39

Rule 57

Gene: SID W 415811 ESTs [5':W84831 3':W84784] Drag: Pyrazoloacridine Parameters: μ_k ^sen = -0.873 μιr^ea = 0.1935 σ_k ^avg = 0.8924 p_(c.^sens^itive) ₌ Q.1811, _P( ^insensitive) *-= 0.8189

Quadratic Discriminant Analysis - 1 -dimensional (QDA ID)

This method computes a Bayesian conditional probability P( ^KJ i e c >^sensitive I s^δ-^k/)^J that a cell line ^J is sensitive to drag ^z , given the gene k abundance ?^k in cell line J .

The probability is computed using the following equation: .

where p ₍.sensitives

*^■ ' ^_ prior probability of the sensitive set I (.sensitive I (.sensitive i . i .insensitive

prior probability ofthe insensitive I ( /..iinnsseennssiittiivvee | 1/1 (.sensitive ι . ι /.insensitive set^"

.sensitive / j s i^k ^'•^•' ^""probability of abundance value gl from the gaussian density fitted to the histogram ofthe gene k abundances over the sensitive cell lines when subjected to drag .

₍. sensitive j s _ ^l I „-( tg „{j -μ ,,_ksen )_\2 I _f2_t(σ „_ksen _\ )2

' * ^{§k) ~} σTJte where μ_k ⁿ = mean of gene k abundances in the sensitive cell lines * = standard deviation of gene k abundances in the sensitive cell lines

_;G'™^ens"^lve (gl) = probability of abundance value gl from the gaussian density fitted to the histogram ofthe gene k abundances over the insensitive cell lines when subjected to drug i .

where μ^insen _ _{mean 0}f g_ene k abundances in the insensitive cell lines insen ^{• ft} = standard deviation of gene k abundances in the insensitive cell lines

Sample parameters for QDA1 analysis on the NCI60 dataset are:

Rule l Gene: Human mRNA for reticulocalbin complete cds Chr.11 [485209 (IW) 5':AA039292 3':AA039334] Drag: Inosine-glycodialdehyde Parameters: μ_k ^sen = -0.7618, σ_k ^sen = 1.57 μ_k ^insen = 0.1878, σ_k ^insen = 0.6952

P( ^sensitive) = 0.1978, P( ^insensitive) = 0.8022

Rule 2

Drag: Inosine-glycodialdehyde Parameters: μ_k ^sen = -0.8Ϊ15, σ_k ^sen = 1.161 μ_k ^insen = 0.2001 , σiT⁶¹¹ = 0.8443 p(Qsensitive) ₌ Q _{l 9}η^ p^.insensitive) _{= 0>8022} Rule 3

Gene: SID W 254085 ESTs Moderately similar to synaptonemal complex protein [M.musculus] [5':N71532 3':N22165] Drag: Baker's-soluble-antifoliate Parameters: μ_k ^sen = 0.7847, σ_k ^sen = 0.6875 μ_k ^insen = -0.2423, ok*"⁸™ = 0.8722

P(Cι^sensitive) = 0.2361, P( ^insensitive) = 0.7639

Rule 4

Gene: THY-1 MEMBRANE GLYCOPROTEIN PRECURSOR Chr.11 [183950 (E) 5':H30297 3':H28104] Drug: Mitozolamide Parameters: μ_k ^sen = 1.073, σ_k ^sen = 1.284 μ_k ^insen = -0.2694, σ_k ^insm = 0.6137 _p(c.s^en^sitiv^e) _{= 0}_₂006, p(C_i ^inSensitive) = 0.7994

Rule 5

Gene: PTN Pleiofrophin (heparin binding growth factor 8 neurite growth-promoting factor 1) Chr.7 [488801 (IW) 5':AA045053 3':AA045054] Drag: Mitozolamide Parameters: μ_k ^sen = 1.019, σ_k ^sen = 1.354 μ_k ^insen = -0.2557, < *** = 0.64 P(Cι^sensitive) = 0.2006, p( ^{insβnsitivβ}) = 0.7994

Rule 6 Gene: SID W 242844 ESTs Moderately similar to ! ! ! ! ALU SUBFAMILY J WARNING ENTRY !!! ! [H.sapiens] [5':H94138 3':H94064] Drug: Mitozolamide Parameters: μ_k ^sen = -1.008, σ_k ^sen = 0.5668 μ_k ^insen = 0.2536, ok*"* = 0.9027 _p(c.sensi^tive) _{= 0>2}006, p(Q^inSensitive) = 0.7994

Rule 7

Gene: Human mRNA for reticulocalbin complete cds Chr.l 1 [485209 (IW) 5*:AA039292 3':AA039334] Drug: Cyclodisone Parameters: μ_k ^sen = 0.6598, σ_k ^sen = 0.2562 μ_k ^insen = -0.1341, G_k ^kisea = 1.038 _p(c.sensitive) _{= Q χ} gg^ _p(c. insensitive) ₌ Q g_{3 U}

Rule 8 Gene: SID W 488387 Exostoses (multiple) 2 [5':AA046786 3':AA046656] Drag: Cyclodisone Parameters: μ_k ^sen = 1.043, σ_k ^sen = 1.087 μ_k ^insen = -0.2128, 0^ = 0.8262 p_(C.sensitive) ₌ Q.1689, p_(c.insen_Sitive) _{= Q} g_{3 χ}

Rule 9

Gene: SID W 487535 Human mRNA for KIAA0080 gene partial cds [5':AA043528 3':AA043529] Drag: Clomesone Parameters: μ_k ^sen = 1.184, σ_k ^sen = 0.9042 _μkinsen ₌ __{Q 2g} j _?> ^insen ₌ ^35 _p(c.sensitive) _{= Q} j^ p_(C. insensitive) _{= Q} g₀₈₃

Rule 10

Gene: PTN Pleiotrophin (heparin binding growth factor 8 neurite growth-promoting factor 1) Chr.7 [488801 (IW) 5':AA045053 3*:AA045054]

Drug: Clomesone

Parameters: μ_k ^sen = 1.14, σ_k ^sen = 1.31 μ_k ^insen = -0.2703, σ^⁶¹¹ = 0.636 _p(c.sensitive) _{= Q m 7>} p^.insensitive) _{= Q} g^

Rule 11

Gene: THY-1 MEMBRANE GLYCOPROTEIN PRECURSOR Chr.ll [183950 (E) 5*:H30297 3':H28104] Drug: Clomesone Parameters: μ_k ^sen = 1.157, σ_k ^sen = 1.312 μ_k ^insen = - 0.2746, 0^ = 0.6219 P(Q^sensitive) = 0.1917, P( ^insensitive) = 0.8083

Rule 12

Gene: SID W 487535 Human mRNA for KIAA0080 gene partial cds [5':AA043528 3':AA043529] Drag: PCNU Parameters: μ_k ^sen = 1.081, σ_k ^sen = 1.083 μ_k ^insen = -0.2435, αi^⁶" = 0.7973 _p(c.sensitive) _{= Q m}^ _p(c.insensitive) _{= Q} g ^

Rule 13

Gene: SID 289361 ESTs [5':N99589 3':N92652] Drag: Fluorouracil (5FU) Parameters: μ_k ^sen = 0.03614, σ_k ^sen = 0.186 μ_k ^insen = -0.007432, σ_k ^iπsen = 1.074 _p(c.sensitive) _{= Q} j ^ _p(c. insensitive) _{= α}g_3?2 Rule 14

Gene: SID 287239 ESTs [5*: 3':N66980] Drag: Fluorodopan Parameters: μ_k ^sen = -0.1888, σ_k ^sen = 1.767 μ_k ^insen = 0.04924, α^⁶¹¹ = 0.6817 p_(c.sensitive) _{= Q} ^^ p_(C. insensitive) _{= 0 939}

Rule 15

Gene: SID 307717 Homo sapiens KIAA0430 mRNA complete cds [5': 3':N92942]

Drug: Cyclocytidine

Parameters: μ_k ^sen = 0.004825, σ_k ^sen = 0.232 μ_k ^insen = -0.002083, σ_k ^insen = 1.151 p_(c.sensitive) ₌ 0 2533^ p_(c.insensitive) ₌ Q ^

Rule 16

Gene: SID W 291620 Restin (Reed-Steinberg cell-expressed intermediate filament- associated protein) [5':W03421 3':N67817] Drug: Porfiromycin Parameters: μ_k ^sen = 0.9491, σ_k ^sen = 0.8827 _μkinsen ₌ ^ ^ _{γ ?} ^insen ^ 0 ^5 p(_C.sensitive) _{= Q 2}Q₃₉₎ p_(C. insensitive) _{= Q ml}

Rule 17

Gene: Human extracellular protein (Sl-5) mRNA complete cds Chr.2 [485875 (EW) 5':AA040442 3':AA040443] Drug: Oxanthrazole (piroxantrone) Parameters: μ_k ^sen = 1.155, σ_k ^sen = 0.8967 _μkinsen ₌ .Q^Q^ ^ins^βα ₌ Q.7438 _p(c.sensitive) _{= Q} j^ p_(c.insensitive) _{= Q} g^

Rule 18 Gene: Human extracellular protein (Sl-5) mRNA complete cds Chr.2 [485875 (EW) 5':AA040442 3*:AA040443] Drug: Anthrapyrazole-derivative Parameters: μ_k ^sen = 1.016, σ_k ^sen = 1.089 μ_k ^insen = -0.2548, σk^insen = 0.7749 _p(c.sensitive) _{= Q 2}006, p(Q^insensaiTC) = 0.7994

Rule 19

Gene: SID 229535 [5':H66594 3':H66595] Drug: Teniposide Parameters: μ_k ^sen = -0.9209, σ_k ^sen = 1.487 μ_k ^insen = 0.2154, σ_k ^insen = 0.6755 _p(c.sensitive) _{= Q γ} g^ _p(c. insensitive) _{= QM Q6}

Rule 20

Gene: ESTs Chr.2 [149542 (DW) 5*:H00283 3':H00284]

Drag: Daunorabicin

Parameters: μ_k ^sen = -1.052, σ_k ^sen = 1.344 μ_k ^insen = 0.2324, σ_k ^insen = 0.6635 _p(c.sensitive) ₌ Q_ _{1 8 1} ^ p^. insensitive) _{= Q} g^

Rule 21 Gene: AKl Adenylate kinase 1 Chr.9 [488381 (IλV) 5':AA046783 30AAO46653] Drag: Daunorabicin Parameters: μ_k ^sen = -0.9847, σ_k ^sen = 1.33 μ_k ^insen = 0.2169, σ_k ^insen = 0.6847 _p(c.sensitive) ₌ Q _{l gl} ^ p ^.insensitive) _{= α8189}

Rule 22

Gene: SID 260288 ESTs [5':H97716 3':H96798]

Drag: Daunorabicin

Parameters: μ_k ^sen = -0.9929, σ_k ^sen = 1.81 μ_k ^insen = 0.2192, σ_k ^insen = 0.4776 p_(c.^sensitive) ₌ 0.1811, p(c_i ^insensitive) = 0.8189

Rule 23

Gene: SID W 345683 ESTs Highly similar to INTEGRAL MEMBRANE GLYCOPROTEIN GP210 PRECURSOR [Rattus norvegicus] [5':W76432 3':W72039] Drug: Daunorabicin Parameters: μ_k ^sen = 0.918, σ_k ^sen = 0.3704 _μk ^insen ₌ __0#2022, _σ ™ = 0.9211 _P(_C.^sensitive) = o.l811, P(C_i ^insensitive) = 0.8189

Rule 24

Gene: GAMMA-INTERFERON-INDUCIBLE PROTEIN IP-30 PRECURSOR Chr.19 [310021 (I) 5': 3':N99151] Drug: CPT,10-OH Parameters: μ_k ^sen = -0.9086, σ_k ^sen = 0.8266 μ_k ^insen = 0.2078, σ_k ^insen = 0.8782 _p(c.sensitive) _{= Q j} ^ p^.insensitive) _{= Q 8 j} 44

Rule 25

Gene: SID 512355 ESTs Highly similar to SRC SUBSTRATE P80/85 PROTEINS [Gallus gallus] [5':AA059424 3':AA057835]

Drug: CPT

Parameters: μ_k ^sen = 0.8614, σ_k ^sen = 0.8019 μ_k ^insen = -0.3016,

= 0.8633 _p(c.sensitive) ₌ Q^^ _p(c. insensitive) _{= Q Q6}

Rule 26

Gene: SID W 488148 H.sapiens mRNA for 3'UTR of unknown protein [5':AA057239 3':AA058703] Drug: CPT Parameters: μ_k ^sen = 0.8224, σ_k ^sen = 0.5588 μ_k ^insen = -0.2881 , σ_k^⁶" = 0.9329 _P(C.sensitive) _{= Q2}59A, P(Q^ksensitive) = 0.7406

Rule 27

Gene: SID W 358526 ESTs [5':W96039 3':W94821] Drag: CPT,ll-formyl (RS) Parameters: μ_k ^sen = -1.055, σ_k ^sen = 1.241 μ_k ^insen = 0.2536, 0^ = 0.7034 _p(c.sensitive) = 0^939^ p_(C.insensitive) _{= Q g()6 χ}

Rule 28

Gene: SID W 135118 GATA-binding protein 3 [5':R31441 3':R3^'1442] ^'

Drag: CPT.l 1-foraιyl (RS)

Parameters: μ_k ^sen = 0.9817, σ_k ^sen = 1.5 μ_k ^insen = -0.2359, σk^ = 0:6465 p_(c.sensitive) _{= Q} ^3^ _p ^.insensitive) _{= Q >g()61} Rule 29

Gene: SID 43609 ESTs [5':H06454 3':H06184] Drag: CPT,11-formyl (RS) Parameters: μ_k ^sen = 0.6312, σ_k ^sen = 1.498 μ_k ^insen = -0.1522, σ^⁶¹^ 0.7671 _p(c.sensitive) _{= Q} j 93 ^ _p(c.insensitive) _{= Q g()61}

Rule 30 Gene: ESTs Chr.16 [154654 (RW) 5':R55184 3':R55185] Drag: CPT, 11 -formyl (RS) Parameters: μ_k ^sen = 0.874, σ_k ^sen = 1.247 μ_k ^insen = -0.2102, σ_k ⁶¹^ 0.7775 P( ^sensitive) = 0.1939, P(Q^insensitive) = 0.8061

Rule 31

Gene: AKl Adenylate kinase 1 Chr.9 [488381 (IW) 5':AA046783 3':AA046653] Drag: Mechlorethamine Parameters: μ_k ^sen = -0.4881, σ_k ^sen = 1.786 μ_k ^insen = 0.1157, σ_k ^insen = 0.6286 _p(c.sensitive) _{= Q l 92}^ p^.insensitive) _{= Q η2}

Rule 32

Gene: SID 43609 ESTs [5':H06454.3':H06184]

Drag: Mechlorethamine

Parameters: μ_k ^sen = 1.042, σ_k ^sen = 0.9895 μ_k ^insen = -0.2493, 0^ = 0.814 _p(c.sensitive) _{= Q} j ^ _p ^.insensitive) _{= QMη2} Rule 33

Gene: SID 43609 ESTs [5':H06454 3':H06184] Drug: Triethylenemelamine Parameters: μ_k ^sen = 0.6685, σ_k ^sen = 1.405 _μkinsen ₌ __Q j gg^ ^insen _{= Q} ^_Q

P( ^sensitive) = 0.2294, P( ^insensitive) = 0.7706

Rule 34 Gene: SID W 133851 ESTs [5':R28233 3':R27977] Drug: Triethylenemelamine Parameters: μ_k ^sen = -0.7551, σ_k ^sen = 1.506 μ_k ^insen = 0.2248, σ_k ^insen = 0.6021 p_(C.sensitive) ₌ Q₂29 _{t p(c}. insensitive) _{= Q ?7()6}

Rule 35

Gene: SID 43609 ESTs [5':H06454 3':H06184] Drug: Thiotepa Parameters: μ_k ^sen = 0.6796, σ_k ^sen = 1.35 _μkinsen ₌ ^₂(Y73_t σ_k™^ea = 0.728 _p(p.sensitive) _{= 0>233}3₅ p(Q insensitive) ₌ Q ^

Rule 36

Gene: SID W 291620 Restin (Reed-Steinberg cell-expressed intermediate filament- associated protein) [5':W03421 3':N67817] Drug: Chlorambucil Parameters: μ_k ^sen = -0.01776, σ_k ^sen = 1.597 μ_k ^insen -= 0.005025, σ^ = 0.7447 _p(c.sensitive) _{= 0#22}06, p^™¹^⁶) = 0.7794 Rule 37

Gene: SID W 133851 ESTs [5*:R28233 3':R27977] Drag: Chlorambucil Parameters: μ_k ^sen = -0.8278, σ_k ^sen = 1.471 μ_kinsen ₌ Q ₂₃₄₂^ ^insen _{= Q 5}g₄ j _p(c.^sensitive) _{= Q 22}Qβ, p(c ^{em e}) = 0.7794

Rule 38

Gene: SID W 510230 Homo sapiens (clone CC6) NADH-ubiquinone oxidoreductase subunit mRNA 3* end cds [5':AA053568 3':AA053557]

Drug: Geldanamycin

Parameters: μ_k ^sen = 0.1441, σ_k ^sen = 1.609 _μkinsen ₌ _₀ 0359^ ^insen _{= 0>7474}

P(Cι^sensitive) = 0.2033, p(c_i ^insensitive) = 0.7967

Rule 39 Gene: SID 381780 ESTs [5':AA059257 3':AA059223] Drag: Paclitaxel — Taxol Parameters: μ_k ^sen = 0.1618, σ_k ^sen = 0.1828 μ_k ^insen = -0.03218, σk^in8en = 1.06 P(Q^sensitive) = 0.1622, P(Q^insensitive) = 0.8378

Rule 40

Gene: H.sapiens mRNA for TRAMP protein Chr.8 [149355 (IEW) 5*:H01598 3':H01495] Drug: Amonafide Parameters: μ_k ^sen = 1.095, σ_k ^sen = 1.188 μk ^ιnsβn = -0.2498, σic^1118611 = 0.7473 _p(c.sensιtιve) _{= Q>} 1 _g6 J _? Q _g j 39

Linear Discriminant Analysis - 2-dimensional (LDA 2D)

This method computes a Bayesian conditional probability

that a cell line ^ is sensitive to drag ^z , given the abundances of genes k and 1, gk ^J ι _} respectively, in cell line ^J .

The probability is computed using the following equation:

where

P(C sensitive

' prior probability ofthe sensitive set

=\ Cf | /(| Cf + \ C" \) :

P Ci"^se"^silive) =prior probability ofthe insensitive insensitive set =i e; i(\ sensitive + \ C"

(.sensitive / j j s , _j i k v^δft^sδ/ _j ₀ j_nt pj-obabiii y of abundance values ^{s k} and ^s ' from the bivariate gaussian density fitted to the histogram of gene k and / abundances over the sensitive cell lines when subjected to drug i .

where μ_k " = mean of gene k abundances over the sensitive cell lines avg ^k = sensitive\insensitive class-weighted average standard deviation of gene k abundances in the sensitive and insensitive cell lines

/T = mean of gene 1 abundances over the sensitive cell lines

σ avg

'^l = sensitive\insensitive class- weighted average standard deviation of gene 1 abundances in the sensitive and insensitive cell lines

^rk,ι —

class-weighted average correlation coefficient of gene k and gene / abundances in the sensitive and insensitive cell lines

_;. '"^eBS'^{ft e}(g/,g-/) = joint probability of abundance values g and gj from the bivariate gaussian density fitted to the histogram of gene k and / abundances over the insensitive cell lines when subjected to drag i .

^.insensitive j _. \ _ i ^k.l &k >&ι ) -

where μ^insen ._g ^ _{mean 0}f g_ene ^ abundances over the insensitive cell lines insen

"^l is the mean of gene k abundances over the insensitive cell lines

Sample parameters for the LDA 2D analysis on the NCI60 dataset are:

Rule l Gene 1 : Glyoxalase-I-log Gene 2: Homo sapiens mRNA for HYA22 complete cds Chr.3 [358957 (EW) 50W91969 30W94916] Drag: Acivicin Parameters: μ_k ^sen = -0.9056, μι^sen = 0.3517 μ_k ^iπsen = 0.2197, μι^insen = -0.08527 σ_k ^av = 0.8751, σι^avg = 0.9817, _Pk ^V = 0.531

P(Cι^sensitive) = 0.1956, p^™¹^⁶) = 0.8044

Rule 2

Gene 1 : SID W 254085 ESTs Moderately similar to synaptonemal complex protein [M.musculus] [50N71532 30N22165] Gene 2: SID 118593 [5':T92821 30T92741] Drug: Baker' s-soluble-antifoliate Parameters: μ_k ^sen = 0.7847, μι^sen = -0.5796 μ ™^>* = -0.2423, μi ⁶¹¹ = 0.1796 σ_k ^avg = 0.8539, σι^avg = 0.8599, _Pk ^V = 0.2493 _p(C.sensitive) _{= 0ι2361 } P}(_C.insensitive) _{= Q ?639}

Rule 3

Gene 1 : SID W 254085 ESTs Moderately similar to synaptonemal complex protein [M.musculus] [50N71532 30N22165] Gene 2: ESTs Chr.5 [46694 (RW) 50H1O24O 30H1O192] Drag: Baker' s-soluble-antifoliate Parameters: μ_k ^sen = 0.7847, μ,^sen = 0.4403 μ_k^^en ₌ -0.2423, μι^insen -= -0.1363 σ_k ^avg = 0.8539, σι^avg = 0.9706, _k ^V = -0.1844 P(Q^sensitive) = 0.2361, P^⁶¹¹⁵^⁶) = 0.7639

Rule 4 Gene 1 : SID W 242844 ESTs Moderately similar to ! ! ! ! ALU SUBFAMILY J WARNING ENTRY !!!! [H.sapiens] [50H94138 30H94O64]

Gene 2: *Hs.648 Cut (Drosoρhila)-like 1 (CCAAT displacement protein) SID W 26677 ESTs [5':R13994 3':R39117] Drag: Mitozolamide Parameters: μ_k ^sen = -1.008, μι^sen = 0.8138 μ_k ^insen = 0.2536, _μι ^insen = -0.2039 σ_k ^av = 0.8681, σι^avg = 0.9103, _Pk ^Vg = 0.07755 p(_C.sensitive) _{= 0 2}QQ^ p^.insensitive) _{= Q Q}g₄

Rule 5

Gene 1: Homo sapiens delta7-sterol reductase mRNA complete cds Chr.lO [417125 (E) 5': 30W87472] Gene 2: SID W 380674 ESTs [50AAO5372O 30AAO53711] Drag: Mitozolamide Parameters: μ_k ^sen = -0.7211, μfⁿ = 1.093 μ_k ^insen = 0.1813, μι^insen = -0.2739 σ_k ^avg = 0.9411, σ,^av = 0.8441, _Pk/^Vg = 0.1253 p_(c.sensitive) ₌ Q^OOβ, p_(C.insensitive) _{= QJ}gg₄

Rule 6

Gene 1: Glutathoine S-Tranferase Pi-log Gene 2: *Hs.648 Cut (Drosoρhila)-like 1 (CCAAT displacement protein) SID W 26677 ESTs [50R13994 30R39117] Drag: Mitozolamide Parameters: μ_k ^sen = -0.917, μι^sen = 0.8138 μ_k ^insen = 0.2307, μι^insen = -0.2039 σ_k ^avg = 0.8411, σι^av = 0.9103, _Pk ^Vg = 0.04772 P(Cι^{sensi ive}) = 0.2006, p( ^imensitiTO) = 0.7994 Rule 7

Gene 1: ESTs Chr.X [48536 (E) 50H14669 30H14579] Gene 2: SID W 242844 ESTs Moderately similar to ! ! ! ! ALU SUBFAMILY J WARNING ENTRY !!!! [H.sapiens] [50H94138 30H94O64] Drag: Clomesone Parameters: μ_k ^sen = -0.8957, μ,^sen = -1.079 μ_k ^insen = 0.2117, μι^insen = 0.2564 σ_k ^av = 0.8904, σι^avg = 0.8587, _Pk ^Vg = -0.165 p_(c.sensitiv_e) ₌ Q.1917, p ((^sensitive) _{= Q g0g3}

Rule 8

Gene 1: SID W 36809 Homo sapiens neural cell adhesion molecule (CALL) mRNA complete cds [5 ' :R34648 3 ' :R49177]

Gene 2: SID W 487535 Human mRNA for KIAA0080 gene partial cds [50AAO43528

30AAO43529]

Drag: Clomesone

Parameters: μ_k ^sen = 0.6335, μι^sen = 1.184 _μkinsen ₌ __0Λ4gg_{} μ} ms∞ ₌ __{Q 2S} j _? σ_k ^avg = 0.9603, σι^av = 0.829, _Pk ^Vg = -0.2448 P(Cι^sensitive) = 0.1917,

= 0.8083

Rule 9

Gene 1: M-PHASE INDUCER PHOSPHATASE 2 Chr.20 [179373 (EW) 50H5O437

30H5O438]

Gene 2: SID W 487535 Human mRNA for KIAA0080 gene partial cds [50AAO43528

30AAO43529] Drag: Clomesone

Parameters: μ_k ^sen = 0.3874, μι^sen = 1.184 - μ_k ^insen =^* -0.09229, μ = -0.2817 σ_k ^avg = 0.9766, σι^avg = 0.829, _Pk ^Vg = -0.2704 P(Cι^sensitive) = 0.1917, P( ^insensitive) = 0.8083

Rule 10

Gene 1 : SID W 242844 ESTs Moderately similar to ! ! ! ! ALU SUBFAMILY J WARNING ENTRY !!!! [H.sapiens] [50H94138 30H94O64] Gene 2: SID 469842 Homo sapiens mRNA for fatty acid binding protein complete cds [5 ' : AA029794 3 ' : AA029795] Drag: Clomesone Parameters: μ_k ^sen = -1.079, μ-^sen = 0.8757 μ_k ^insen = 0.2564, _μι ^insen = -0.2074 σ_k ^avg = 0.8587, σι^avg = 0.9151, _Pk/^Vg = 0.1636 P( ^sensitive) = 0.1917, P( ^insensitive) = 0.8083

Rule 11

Gene 1: ESTsSID 327435 [50W32467 30W1983O]

Gene 2: SID 469842 Homo sapiens mRNA for fatty acid binding protein complete cds [5':AA029794 3':AA029795] Drug: Clomesone Parameters: μ_k ^sen = -0.793, μι^sen = 0.8757 μ_k ^insen = 0.1878, μι^insen = -0.2074 σ_k ^av = 0.9388, σι^avg = 0.9151, _Pk ^Vg = 0.4476 _p(c.sensitive) _{= Q} -^^ p_(C.insensitive) _{= Q} gQgg

Rule 12

Gene 1: SID 512164 Human clathrin assembly protein 50 (AP50) mRNA complete cds [5': 3':AA057396]

Gene 2: SID W 345624 Human homeobox protein (PHOXl) mRNA 3' end [50W764O2 30W72O5O] Drug: Clomesone Parameters: μ_k ^sen = 0.8248, μf^en = -0.253 μ_k ^insen = -0.1956, μι^insen = 0.06021 σ_k ^avg = 0.9014, σι^av = 1.015, _Pk ^Vg = 0.72

P(Ci^sensitive) = 0.1917, p(c_i ^insensitive) = 0.8083

Rule 13

Gene 1: SID W 376951 ESTs [50AAO47756 3':AA047641] Gene 2: SID W 487535 Human mRNA for KIAA0080 gene partial cds [5':AA043528 30AAO43529] Drug: Clomesone Parameters: μ_k ^sen = 0.8665, μι^sen = 1.184 μ_k ^insen = -0.2063, μι^insen = -0.2817 σ_k ^avg = 0.9396, σι^avg = 0.829, _Pk,ι^avg = 0.1106 _p(c.sensitive) _{= Q} j^ _p ^.insensitive) _{= Q} g^

Rule 14 Gene 1 : Glutathoine S-Tranferase Pi-log

Gene 2: SID W 487535 Human mRNA for KIAA0080 gene partial cds [50AAO43528 3':AA043529] Drag: Clomesone Parameters: μ_k ^sen = -0.8961, μfⁿ = 1.184 μ_k ^insen = 0.2131, μι^insen = -0.2817 σ_k ^avg = 0.8991, σι^avg = 0.829, _Pk(1 ^avg = 0.1075 _p(c.sensitive) _{= Q} j^ _p(c.insensitive) ₌ Q^Q^

Rule 15

Gene 1: XRCC4 DNA repair protein XRCC4 Chr.5 [26811 (RW) 50R14O27 30R39148] Gene 2: SID W 242844 ESTs Moderately similar to ! ! ! ! ALU SUBFAMILY J WARNING ENTRY !!!! [H.sapiens] [50H94138 30H94O64]

Drag: Clomesone

Parameters: μ_k ^sen = -0.583, μι^sen = -1.079 μ_k ^insen = 0.1387, μι^insen = 0.2564 σ_k ^av = 0.9879, σι^avg = 0.8587, _P ^Vg = -0.3373 _p(c.sensitive) _{= Q} j ^ p_(c.insensitiv_e) _{= Q} g^

Rule 16 Gene 1: Homo sapiens clone 24711 mRNA sequence Chr.2 [345084 (IW) 50W76362 30W723O6]

Gene 2: *Homo sapiens lysosomal neuraminidase precursor mRNA complete cds SID W 487887 Hexabrachion (tenascin C cytotactin) [50AAO46543 30AAO45473] Drag: Clomesone Parameters: μ_k ^sen = -0.5805, μι^sen = 0.8678 μ_k ^insen = 0.137, μι^insen = -0.2056 σ_k ^av = 0.968, σι^avg = 0.911, _k ^Vg = 0.5627 P(Cι^sensitive) = 0.1917,

= 0.8083

Rule 17

Gene 1 : SID 260048 Homo sapiens intermediate conductance calcium-activated potassium channel (hKCa4) mRNA complete [5': 30N32O1O] Gene 2: SID W 487535 Human mRNA for KIAA0080 gene partial cds [50AAO43528 30AAO43529] Drag: Clomesone Parameters: μ_k ^sen = 0.3774, _W ^sen = 1.184 μ_k ^insen = - 0.09052, μι^insen = -0.2817 σ_k ^avg = 1.015, σι^avg = 0.829, _Pk/^Vg = -0.2375

P(Cι^sensitive) = 0.1917, p(C_i ^insensitive) = 0.8083 Rule 18

Gene 1: ESTs Weakly similar to R06B9.b [C.elegans] Chr.l [365488 (IW) 5':AA009557 30AAOO9558]

Gene 2: SID W 487535 Human mRNA for KIAA0080 gene partial cds [50AAO43528 30AAO43529] Drug: Clomesone Parameters: μ_k ^sen = 0.6026, μ_! ^sen = 1.184 μ_k ^insen = -0.1433, μι^insen = -0.2817 σ_k ^avg = 0.9451, σι^avg = 0.829, _Pk ^Vg = -0.0427 _p(c.sensitive) _{= 0 1917;} p(_C. insensitive) _{= Q} g_Qg₃

Rule 19

Gene 1 : ESTs Moderately similar to DUAL SPECIFICITY PROTEIN PHOSPHATASE VHR [H.sapiens] Chr.17 [49293 (E) 50H15616 30H15557]

Gene 2: SID W 487535 Human mRNA for KIAA0080 gene partial cds [50AAO43528

30AAO43529]

Drug: Clomesone

Parameters: μ_k ^sen = -0.1122, _μι ^sen = 1.184 μ_k ^insen = 0.02618, μι^insen = -0.2817 σ_k ^avg = 1.019, _σι ^av = 0.829, _Pk ^Vg = 0.4234 p_(c.se„sitive) _{= 0p} J _917j p_(C.insensitive) _{= 0 8()83}

Rule 20

Gene 1 : SID W 242844 ESTs Moderately similar to ! ! ! ! ALU SUBFAMILY J

WARNING ENTRY !!!! [H.sapiens] [50H94138 30H94O64]

Gene 2: SID W 487535 Human mRNA for KIAA0080 gene partial cds [5':AA043528

3':AA043529] Drag: Clomesone

Parameters: μ_k ^sen = -1.079, μι^sen = 1.184 μ_k ^insen = 0.2564, μι^insen = -0.2817 σ_k ^avg = 0.8587, σ,^avg = 0.829, _k ^Vg = 0.02375 _p(c.sensitive) ₌ Q^gγj ^ p_(C. insensitive) _{= Q >g()g3}

Rule 21 Gene 1 : SID W 487535 Human mRNA for KIAA0080 gene partial cds [5':AA043528 30AAO43529]

Gene 2: ESTs Chr.6 [144805 (EW) 50R76279 30R76556] Drug: Clomesone Parameters: μ_k ^sen = 1.184, μι^sen = 0.4822 _μk ^insen ₌ -0.2817, μι^insen = -0.1143 σ_k ^avg = 0.829, σι^avg = 0.9949, _Pk ^Vg = -0.2002 _p(c.sensitive) _{= 0# 1917> p(C}.insensitive) _{= QMS3}

Rule 22

Gene 1: SID W 487535 Human mRNA for KIAA0080 gene partial cds [5':AA043528 30AAO43529]

Gene 2: SID W 488333 ESTs [5':AA046755 30AAO46642] Drag: Clomesone Parameters: μ_k ^sen = 1.184, μι^sen = -0.1604 μ_k ^insen = -0.2817, μ ^ea = 0.03825 σ_k ^avg = 0.829, _σι ^avg = 1.011, _Pk ^Vg = 0.3461 P( ^sensitive) = 0.1917, P(Q^insensitive) = 0.8083

Rule 23

Gene 1: ANX3 Annexin III (lipocortin III) Chr.4 [328683 (IW) 50W4O286 30W45327] Gene 2: SID W 487535 Human mRNA for KIAA0080 gene partial cds [50AAO43528 30AAO43529] Drug: Clomesone Parameters: μ_k ^sen = -0.7239, μi sen . = 1.184 μ_k ^insen = 0.1714, μf ^ea = -0.2817 σ_k ^avg = 0.9663, σι^avg = 0.829, _k/^Vg = -0.1129 _p(c.sensitive) _{= Q} 19^ _P(_C. insensitive) _{= Q} g^

Rule 2244 Gene s 1: SID 308729 ESTs [50W25229 30N95389] Gene i 22:: SID W 487535 Human mRNA for KIAA0080 gene partial cds [50AAO43528

Drag: Clomesone Parameters: μ_k ^sen = -0.6074, μι^sen = 1.184 μ_k ^insen = 0.1438, μι^insen = -0.2817 σ_k ^avg = 0.9876, σι^avg = 0.829, _Pk ^Vg = 0.1155 p(_C.sensitive) _{= 0Λ 9l l> p(C}.msensitive) _{= αgog3}

Rule 25

Gene 1 : Metallothionein content-log

Gene 2: SID W 487535 Human mRNA for KIAA0080 gene partial cds [50AAO43528 30AAO43529] Drag: Clomesone Parameters: μ_k ^sen = 0.5109, μι^sen = 1.184 μ_k ^insen = - 0.1211, _μι ^insen = -0.2817 σ_k ^avg = 0.9435, σι^avg = 0.829, _k ^Vg = -0.3179 _p(c.sensitive) = 0.1917, P( ^insβnsitiw) = 0.8083

Rule 26

Gene 1: ESTs Chr.14 [160605 (E) 50H25O13 30H25O14] Gene 2: SID W 487535 Human mRNA for KIAA0080 gene partial cds [50AAO43528 30AAO43529] Drag: Clomesone Parameters: μ_k ^sen = -0.7174, μι^sen = 1.184 _μkinsen ₌ Q j ^ _μ msen ₌ __{0 2}817 σ_k ^avg = 0.9506, σι^avg = 0.829, _k)ι^avg = 0.01308 P( ^sensitive) = 0.1917, P(C_i ^insensitive) = 0.8083

Rule 27

Gene 1 : SID W 510534 MAJOR GASTROINTESTINAL TUMOR-ASSOCIATED PROTEIN GA733-2 PRECURSOR [5':AA055858 3':AA055808] Gene 2: SID W 242844 ESTs Moderately similar to ! ! ! ! ALU SUBFAMILY J WARNING ENTRY !!!! [H.sapiens] [50H94138 30H94O64] Drug: Clomesone Parameters: μ_k ^sen = -0.867, μι^sen = -1.079 μ_k ^insen = 0.2052, μι^insen = 0.2564 σ_k ^avg = 0.9304, σι^avg = 0.8587, _k ^Vg = -0.08247

P(Cι^sensitive) = 0.1917, p(C_i ^insensitive) = 0.8083

Rule 28 Gene 1: SID W 489262 Allograft inflammatory factor 1 [50AAO45718 30AAO45719] Gene 2: SID W 489301 ESTs [50AAO54471 30AAO58511] Drug: PCNU Parameters: μ_k ^sen = -0.1844, μfⁿ = 0.7991 μ_k ^insen = 0.04227, μι^insen = -0.1796 σ_k ^avg = 0.9895, _σι ^av = 0.9465, _Pk ^V = 0.7317 _p(c.sensitive) _{= Q 3}^ p_(C. insensitive) _{= QM 6η}

Rule 29 Gene 1: p53 mutation-log

Gene 2: SID 43555 MALATE OXIDOREDUCTASE [50H1337O 30HO6O37] Drag: Fluorouracil (5FU) Parameters: μ_k ^sen = 0.9274, μι^sen = 0.9686 _μkinsen ₌ .₀.₁₇72, _μ] ^insen =- -0.1883 σ_k ^av = 0.899, σι^avg = 0.9219, _Pkjι^avg = -0.186 P(Q^sensitive) = 0.1628, P( ^insensitive) = 0.8372

Rule 30

Gene 1: ME2 Malic enzyme 2 mitochondrial Chr.18 [109375 (IW) 50T8O865 3':T70290] Gene 2: SID W 488806 Thioredoxin [50AAO45O51 30AAO45O52] Drug: Asaley Parameters: μ_k ^sen = 0.7873, μ,^sen = -0.922 μ_k ^insen = -0.182, μ,^insen = 0.2136 σ_k ^avg = 0.9409, σι^avg = 0.9102, _Pk ^Vg = 0.3849 _p(c.sensitive) _{= Q} j g^ _p(c. insensitive) _{= g}

Rule 31

Gene 1 : X-ray induction of mdm2-log Gene 2: Human thymosin beta-4 mRNA complete cds Chr.20 [305890 (IW) 5':W19923 30N91268]

Drug: Cytarabine (araC) Parameters: μ_k ^sen = 0.5649, μfⁿ = -0.7694 μ_k ^insen = -02054, μι^insen = 0.2788 σ_k ^av = 0.8243, σι^avg = 0.8663, _Pk ^Vg = 0.2969 _p(C.sensitive) _{= Q 2661 ?} p^.insensitive) _{= Q η 3}g

Rule 32 Gene 1: *EST H49897 SID 429460 ESTs [5': 30AAOO7629]

Gene 2: TXNRDl Thioredoxin reductase Chr.12 [510377 (IW) 50AAO554O7 30AAO554O8] Drug: Anthrapyrazole-derivative Parameters: μ_k ^sen = -0.8238, μfⁿ = 0.8618 μ_k ^insen = 0.2071, μι^insen = -0.2166 σ_k ^avg = 0.934, σ,^avg = 0.9084, _k ^Vg = 0.2681

P(Q^sensitive) = 0.2006, p(c_i ^insensitive) = 0.7994

Rule 33

Gene 1 : PTN Pleiotrophin (heparin binding growth factor 8 neurite growth-promoting factor 1) Chr.7 [488801 (IW) 5':AA045053 30AAO45O54]

Gene 2: TXNRDl Thioredoxin reductase Chr.12 [510377 (IW) 50AAO554O7

30AAO554O8]

Drug: Anthrapyrazole-derivative

Parameters: μ_k ^sen = 0.8876, μι^sen = 0.8618 μj*** = -0.2227, μι^insen = -0.2166 σ_k ^avg = 0.8932, σι^av = 0.9084, _Pk)1 ^avg = -0.3478 _p(c.s^ensitive) _{= Q 2}006, P(C_i ^insensitive) = 0.7994

Rule 34

Gene 1 : SID W 345683 ESTs Highly similar to INTEGRAL MEMBRANE GLYCOPROTEIN GP210 PRECURSOR [Rattus norvegicus] [50W76432 30W72O39] Gene 2: ESTs Chr.5 [322749 (I) 5': 30W15473] Drug: Daunorabicin Parameters: μ_k ^sen = 0.918, μι^sen = -0.7006 μ_k ^insen = -0.2022, _μι ^insen = 0.1549 σ_k ^avg = 0.8758, σι^avg = 0.9296, _Pk,ι^{avg ■}= 0.2797 _p(c.^sensitive) = Q.1811 , p(C_i ^inSensitive) = 0.8189

Rule 35

Gene 1: L-LACTATE DEHYDROGENASE M CHAIN Chr.ll [510595 (IW) 50AAO57759 30AAO5776O]

Gene 2: Homo sapiens T245 protein (T245) mRNA complete cds Chr.X [343063 (IW) 5':W67989 3':W68001] Drag: Daunorabicin Parameters: μ_k ^sen = -0.7199, μfⁿ = -1.061 μ_k ^insen = 0.1588, μι^insen = 0.234 σ_k ^avg = 0.9279, σι^avg = 0.8647, _Pk ^Vg = -0.2833 _p(c.sensitive) _{= Q} ^ p(_C. insensitive) _{= Q g}

Rule 36

Gene 1: SID W 345683 ESTs Highly similar to INTEGRAL MEMBRANE GLYCOPROTEIN GP210 PRECURSOR [Rattus norvegicus] [50W76432 30W72O39] Gene 2: SID W 510534 MAJOR GASTROINTESTINAL TUMOR- ASSOCIATED PROTEIN GA733-2 PRECURSOR [50AAO55858 30AAO558O8] Drug: Daunorabicin Parameters: μ_k ^sen = 0.918, μι^sen = -0.437 μ_k ^insen = - 0.2022, μι^insen = 0.09623 σ_k ^avg = 0.8758, σι^av = 0.9836, _Pk ^Vg = 0.525

P(Q^sensitive) = 0.1811, P( ^insensitive) = 0.8189

Rule 37

Gene 1: ESTs Chr.2 [149542 (DW) 50HOO283 30HOO284] Gene 2: ESTsSID 429074 [5':AA005275 30AAOO5169] Drag: Daunorabicin Parameters: μ_k ^sen = -1.052, μι^sen = -0.6467 μ_k ^insen = 0.2324, μι^insen = 0.1424 σ_k ^av = 0.8508, σι^avg = 0.9537, _Pk,ι^avg = 0.06255 _p(c.sensitive) _{= 0- 1 81 1 j} p_(C.insensitive) _{= Q} g j.gp Rule 38

Gene 1: SID W 345683 ESTs Highly similar to INTEGRAL MEMBRANE GLYCOPROTEIN GP210 PRECURSOR [Rattus norvegicus] [50W76432 30W72O39] Gene 2: Human clone 23933 mRNA sequence Chr.17 [23933 (IW) 50T77288 30R39465]

Drug: Daunorabicin Parameters: μ_k ^sen = 0.918, μι^sen = 0.4489 μ_k ^insen = - 0.2022, μι^insen = -0.09989 σ_k ^av = 0.8758, σι^avg = 1.004, _Pk ^Vg = -0.5196 p(_C.sensitive) =- Q.1811 , p(C ^sensitive) = 0.8189

Rule 39

Gene 1: GRL Glucocorticoid receptor Chr.5 [262691 (E) 5': 30H99414] Gene 2: *Prothymosin alpha SID W 271976 AMINOACYLASE- 1 [50N44687 30N35315] Drag: Daunorabicin Parameters: μ_k ^sen = 0.3732, μι^sen = -1.032 μ_k ^insen = -0.08233, μι^insen = 0.2284 σ_k ^av = 0.9501, σι^avg = 0.858, _Pk ^Vg = 0.3514 _p(c.sensitive) ₌ Q.181 1 , p(C_i ^insensitive) = 0.8189

Rule 40 Gene 1 : *Prothymosin alpha SID W 271976 AMINOACYLASE- 1 [5 ' :N44687 30N35315]

Gene 2: PLAUR Plasminogen activator urokinase receptor Chr.19 [325077 (DIW) 5':W49705 3':W49706] Drag: Daunorabicin Parameters: μ_k ^sen = -1.032, μι^sen = 0.1522 μ_k ^insen = 0.2284, μι^insen = -0.03346 σ_k ^av = 0.858, σι^avg = 0.9987, _Pk/^Vg = 0.5897 _p(c.^sensitive) _{= 0}.1811, p(Q^insensitive) = 0.8189

Rule 41 Gene 1: ESTs Chr.2 [149542 (DW) 5':H00283 3':H00284]

Gene 2: ESTs Chr.2 [365120 (IW) 50AAO252O4 30AAO25124]

Drag: Daunorabicin

Parameters: μ_k ^sen = -1.052, μι^sβn = 0.2085 μ_k ^insen = 0.2324, μι^insen = -0.04633 σ_k ^avg = 0.8508, σι^avg = 1.018, _Pk ^Vg = 0.376 _p(c.sensitive) _{= Q} -^ ^ _p(c.insensitive) _{= 0 8189}

Rule 42 Gene 1: ESTs Chr.2 [149542 (DW) 50HOO283 30HOO284]

Gene 2: Ribosomal protein L17SID 60561 [50T39375 30T4O54O]

Drag: Daunorabicin

Parameters: μ_k ^sea = -1.052, μ,^sen = -0.5213 μ_k ^insen = 0.2324, μι^insen = 0.1147 σ_k ^av = 0.8508, σι^av = 0.9713, _Pk ^Vg = -0.2356 _p(c.sensitive) _{= Q χ} g _χ j ^ p_(e.insensitive) ₌ Q.₈189

Rule 43 Gene 1 : ESTs Chr.2 [ 149542 (DW) 5 ' :H00283 3 ' :H00284] Gene 2: Glutathione S-Tranferase Mla-log Drag: Daunorabicin Parameters: μ_k ^sen = -1.052, _μι ^sen = 0.1809 μ_k ^iπsen = 0.2324, _μι ^insen = -0.03737 σ_k ^avg = 0.8508, σι^avg = 1.033, _Pk,ι^avg = 0.1657 _p(c.sensitive) _{QM g}g

Rule 44

Gene 1: SID 260288 ESTs [50H97716 30H96798]

Gene 2: SID W 358185 Human mitochondrial 2.4-dienoyl-CoA reductase mRNA complete cds [5':W95455 3':W95406] Drug: Daunorabicin Parameters: μ_k ^sen = -0.9929, μj^sen = -0.5507 μ_k ^insen = 0.2192, μι^insen = 0.1224 σ_k ^av = 0.9063, σι^avg = 0.9734, _Pk, ^g = -0.4799 p_(c.sensitive) = 0.1811, p(C_i ^insensitive) = 0.8189

Rule 45

Gene 1: ESTs Chr.2 [149542 (DW) 50HOO283 30HOO284] Gene 2: L-LACTATE DEHYDROGENASE M CHAIN Chr.l 1 [510595 (IW) 50AAO57759 30AAO5776O] Drag: Daunorabicin Parameters: μ_k ^sen = -1.052, μι^sen = -0.7199 μ_k ^insen = 0.2324, μ,^insen = 0.1588 σ_k ^avg = 0.8508, σι^avg = 0.9279, _Pk ^V = -0.1035 _p(c.sensitive) ₌ Q_{A >} p(Q insensitive) _{= Q} g^

Rule 46 Gene 1: SID W 471763 Crystallin zeta (quinone reductase) [50AAO35179 30AAO3518O]

Gene 2: ESTs Chr.2 [149542 (DW) 50HOO283 30HOO284] Drag: Daunorabicin Parameters: μ_k ^sen = -0.5185, μι^sen = -1.052 μ_k ^insen = 0.1147, μι^insen = 0.2324 σ_k ^avg = 0.9683, σι^avg = 0.8508, _Pk)1 ^avg = -0.06753 P(Q^sensitive) = 0.1811, P( ^insensitive) = 0.8189

Rule 47

Gene 1 : SID W 345683 ESTs Highly similar to INTEGRAL MEMBRANE GLYCOPROTEIN GP210 PRECURSOR [Rattus norvegicus] [5':W76432 30W72O39] Gene 2: SID W 489301 ESTs [50AAO54471 3':AA058511] Drag: Daunorabicin Parameters: μ_k ^sen = 0.918, μfⁿ = 0.7391 μ_k ^insen = -0.2022, _μι ^insen = -0.1637 σ_k ^avg = 0.8758, σι^avg = 0.9515, _Pk ^Vg = -0.3077

P(Q^sensitive) = 0.1811, p( ^insensitive) = 0.8189

Rule 48

Gene 1: ESTs Chr.2 [149542 (DW) 50HOO283 30HOO284] Gene 2: *Aldehyde reductase 1 (low Km aldose reductase) SID W 418212 ESTs [5':W90268 3':W90593] Drug: Daunorabicin Parameters: μ_k ^sen = -1.052, μι^sen = 0.09908 μ_k ^insen = 0.2324, μ,^insen = -0.02151 σ_k ^av = 0.8508, σι^av = 1.014, _Pk ^Vg = 0.4702 _p(C,^se„^sitive) ₌ o.l811, P(C_i ^insensitive) = 0.8189

Rule 49

Gene 1: ESTs Chr.2 [149542 (DW) 50HOO283 30HOO284] Gene 2: SID W 484773 PYRROLINE-5-CARBOXYLATE REDUCTASE [50AAO37688 3':AA037689] Drag: Daunorabicin Parameters: μ_k ^sen = -1.052, μfⁿ = -0.7351 μ_k ^ins∞ = 0.2324, μι^insen = 0.1628 σ_k ^avg = 0.8508, σι^avg = 0.9291, _Pk)ι^avg = -0.1858 _p(-_c.sensitive) _{= Q 18 U ?} p^.insensitive) _{= g l g9}

Rule 50 Gene 1 : SID W 484773 PYRROLINE-5-CARBOXYLATE REDUCTASE [50AAO37688 30AAO37689]

Gene 2: *Prothymosin alpha SID W 271976 AMINOACYLASE- 1 [50N44687 30N35315] Drag: Daunorabicin Parameters: μ_k ^sen = -0.7351, μι^sen = -1.032 _μkinsen _{= Q} j ^ ^insen ₌ Q^^

σ_k ^avg = 0.9291, σι^avg = 0.858, _Pk ^Vg = -0.2602 _p(c.sensitive) _{= Q 1 81} ^ p(Q insensitive) _{= Q 8189}

Rule 51

Gene 1: ESTs Chr.16 [154654 (RW) 50R55184 30R55185] Gene 2: ELONGATION FACTOR TU MITOCHONDRIAL PRECURSOR Chr.16 [429540 (IW) 5':AA011453 3':AA011397] Drag: Daunorabicin Parameters: μ_k ^sen = 0.8271, μι^sen = -0.994 _μkinsen ₌ __{Q χ} ^ ^insen _{= Q 2} j gg σ_k ^avg = 0.9198, σj^avg = 0.8654, _Pk),^avg = 0.223 _P(C.sensitive) _{= 0}._{1 81 1 s} p(Q insensitive) _{= 0M} %g

Rule 52

Gene 1: SID 234072 EST Highly similar to RETROVIRUS-RELATED POL POLYPROTEIN [Homo sapiens] [5': 30H69OO1] Gene 2: ESTs Chr.2 [149542 (DW) 50HOO283 30HOO284] Drug: Daunorabicin Parameters: μ_k ^sen = -0.5103, μι^sen = -1.052 μk _: ^ιnsen = 0.1131, μr^en = 0.2324 σ_k ^avg = 0.9797, σι^avg = 0.8508, _Pk ^V = -0.1946 _p(c.sensitive) _{= Q} j _{g J} ^ _p(c.insensitive) _{= Q g l gp}

Rule 53

Gene 1 : ELONGATION FACTOR TU MITOCHONDRIAL PRECURSOR Chr.16 [429540 (IW) 50AAO11453 30AAO11397] Gene 2: ESTs Chr.2 [365120 (IW) 50AAO252O4 30AAO25124] Drug: Amsacrine Parameters: μ_k ^sen = -0.7939, μfⁿ = 0.558 μ_k ^insen = 0.2239, μ ^∞ = -0.1576 σ_k ^avg = 0.8691, σι^avg = 0.9701, _Pk ^Vg = 0.4985 P(Q^sensitive) = 0.22, P( ^insensitive) = 0.78

Rule 54

Gene 1: SID W 489301 ESTs [50AAO54471 3':AA058511] Gene 2: H.sapiens mRNA for TRAMP protein Chr.8 [149355 (IEW) 5':H01598 3':H01495]

Drag: Pyrazoloimidazole Parameters: μ_k ^sen = 0.9637, μj^sen = 0.7678 μ_k ^iπsen = -0.2165, μι^insen = -0.1717 σ_k ^avg = 0.8641, σι^avg = 0.9429, _Pk ^Vg = -0.4318

P( ^sensitive) = 0.1833, ^ c^^) = 0.8167

Rule 55

Gene 1: GAMMA-INTERFERON-INDUCIBLE PROTEIN IP-30 PRECURSOR Chr.19 [310021 (I) 5': 3':N99151]

Gene 2: SID W 487113 Msh (Drosophila) homeo box homolog 1 (formerly homeo box 7) [50AAO45226 30AAO45325] Drug: CPT,10-OH Parameters: μ_k ^sen = -0.9086, μι^sen = 0.8196 μ_k ^insen = 0.2078, μ ^en = -0.1876 σ_k ^avg = 0.8915, σι^avg = 0.8784, _Pk ^V = 0.3086 _p(c.sensitive) _{= Q} j^ _p(c.insensitive) _{= Q S U4}

Rule 56

Gene 2: SID W 346587 Homo sapiens quiescin (Q6) mRNA complete cds [50W79188

30W74434]

Drag: CPT,10-OH

Parameters: μ_k ^sen = -0.9086, μfⁿ = 1.001 μ_k ^insen = 0.2078, μι^insen = -0.2285 σ_k ^avg = 0.8915, σι^avg = 0.8549, _Pk ^Vg = -0.09544 _p(c.sensitive) ₌ Q.1856, (_C ™Mve) _{= Q>gl44}

Rule 57

Gene 1: SID W 510189 Homo sapiens CAG-isl 7 mRNA complete cds [50AAO53648 30AAO53259]

Gene 2: SID W 510534 MAJOR GASTROINTESTINAL TUMOR-ASSOCIATED PROTEIN GA733-2 PRECURSOR [50AAO55858 30AAO558O8] Drag: CPT,10-OH Parameters: μ_k ^sen = 0.4935, μι^sen = -0.6863 μ_k ⁱⁿs^en ₌ -o.i 128, _μι ^insen = 0.1559 σ_k ^avg = 0.9732, σι^avg = 0.9458, _Pk;ι^av = 0.6221 P( ^sensitive) = 0.1856, P( ^insensitive) = 0.8144

Rule 58 Gene 1: GAMMA-INTERFERON-INDUCIBLE PROTEIN IP-30 PRECURSOR Chr.19 [310021 (I) 5': 3':N99151]

Gene 2: COL4A1 Collagen type IV alpha 1 Chr.13 [489467 (IEW) 50AAO54624 3':AA054564] Drag: CPT,10-OH Parameters: μ_k ^sen = -0.9086, μι^sen = 0.8311 μ_k-* ° = 0.2078, μι^iπsen = -0.1889 σ_k ^avg = 0.8915, σι^avg = 0.9008, _Pk ^Vg = 0.04514 p(_C.sensitive) _{= Q χ} g^ ^.insensitive) _{= QM 4}

Rule 59

Gene 1: GAMMA-INTERFERON-INDUCIBLE PROTEIN IP-30 PRECURSOR Chr.19

[310021 (I) 5': 3':N99151] Gene 2: SID 512355 ESTs Highly similar to SRC SUBSTRATE P80/85 PROTEINS

[Gallus gallus] [5':AA059424 30AAO57835]

Drag: CPT,10-OH

Parameters: μ_k ^sen = -0.9086, μfⁿ = 0.8282 μ_k ^insen = 0.2078, μ,^insen = -0.1885 σ_k ^av = 0.8915, _σι ^avg = 0.9162, _Pk ^V = -0.1186 _p(c.sensitive) _{= Q χ} ^ _p(c.insensitive) _{= QM44}

Rule 60 Gene 1 : GAMMA-INTERFERON-INDUCIBLE PROTEIN IP-30 PRECURSOR Chr.19 [310021 (I) 5': 3':N99151]

Gene 2: SID W 324073 Human lysyl oxidase-like protein mRNA complete cds [5':W46647 3':W46564] Drag: CPT,10-OH Parameters: μ_k ^sen = -0.9086, μι^sen = 0.7583 μ_k ^insen = 0.2078, μι^insen = -0.1738 σ_k ^avg = 0.8915, σι^avg = 0.9205, _Pk ^Vg = 0.2083 _p(c.sensitive) ^ _Q j g^ ^.insensitive) _{= QM 44}

Rule 61 Gene 1 : GAMMA-INTERFERON-INDUCIBLE PROTEIN IP-30 PRECURSOR Chr.19 [310021 (I) 5': 3':N99151]

Gene 2: SID W 376472 Homo sapiens clone 24429 mRNA sequence [5':AA041443 30AAO4136O] Drug: CPT,10-OH Parameters: μ_k ^sen = -0.9086, μι^sen = 0.7273 μ_k ^insen = 0.2078, μι^insen = -0.1653 σ_k ^avg = 0.8915, σι^av = 0.927, _Pk ^Vg = 0.02373 _p(c.se„sitive) _{= ι g56> p(c}.insensitive) _{= QM44}

Rule 62

Gene 1 : SID W 487535 Human mRNA for KIAA0080 gene partial cds [5':AA043528

3':AA043529]

Gene 2: Homo sapiens (clone 35.3) DRAL mRNA complete cds Chr.2 [324636 (IW) 50W46933 30W46835]

Drag: CPT,10-OH

Parameters: μ_k ^sen = 0.8729, μι^sen = 0.7843 μ_k ^insen = -0.1997, μι^insen = -0.1778 σ_k ^avg = 0.8949, σι^av = 0.9125, _k ^Vg = -0.1147 p_(c.sensitive) _{= 0 1 856}. p^.insensitive) ₌ Q_{>8 144}

Rule 63

Gene 2: SID W 487878 SPARC/osteonectin [5':AA046533 30AAO45463]

Drag: CPT,10-OH

Parameters: μ_k ^sen =^■ -0.9086, μι^sen = 0.8472 μ_k ^insen = 0.2078, μι^insen = -0.1926 σ_k ^avg = 0.8915, σι^avg = 0.898, _k ^Vg = -0.04153 _p(c.sensitive) _{= Q} ^^ _p(c.insensitive) _{= Q} g ₄

Rule 64

Gene 1: GAMMA-INTERFERON-INDUCIBLE PROTEIN IP-30 PRECURSOR Chr.19 [310021 (I) 5': 3':N99151] Gene 2: Drug: CPT,10-OH Parameters: μ_k ^sen = -0.9086, μι^sen = 0.6293 μ_k ^insen = 0.2078, μι^insen = -0.1436 σ_k ^avg = 0.8915, σj^avg = 0.9536, _Pk ^Vg = 0.1463 P( ^sensitive) = 0.1856, P(Cι^insensitive) = 0.8144

Rule 65

Gene 1: ESTs Chr.X [254029 (IRW) 50N75199 30N22323]

Gene 2: SID W 346587 Homo sapiens quiescin (Q6) mRNA complete cds [50W79188 30W74434]

Drag: CPT,10-OH Parameters: μ_k ^sen = 0.1804, μι^sen = 1.001 μ_k ^insen = -0.04026, μι^insen = -0.2285 σ_k ^avg = 1.01, σι^avg = 0.8549, _k ^Vg = -0.4875 _p(c.sensitive) _{= Q} ^ _p(c.insensitiv_e) _{= QM44}

Rule 66

Gene 1: SID W 364810 ESTs [50AAO3443O 30AAO53921] Gene 2: GAMMA-INTERFERON-INDUCIBLE PROTEIN IP-30 PRECURSOR Chr.19 [310021 (I) 5': 3':N99151] Drug: CPT,10-OH Parameters: μ_k ^sen = -0.6399, μι^sen = -0.9086 _μkinsen _{= Q} j^ _Rinsen _{= Q 2Qη2>} σ_k ^avg = 0.9312, σι^avg = 0.8915, _Pk ^Vg = -0.1262 P( ^sensitive) = 0.1856, P(Q^insensitive) = 0.8144

Rule 67

Gene 1: GAMMA-INTERFERON-INDUCIBLE PROTEIN IP-30 PRECURSOR Chr.19 [310021 (I) 5': 3':N99151] Gene 2: SID 257009 ESTs [5':N39759 3':N26801] Drag: CPT,10-OH Parameters: μ_k ^sen = -0.9086, μι^sen = 0.5127 μ_k ^insen = 0.2078, μ,^insen = -0.1168 σ_k ^avg = 0.8915, σι^avg = 0.9602, _Pk ^Vg = 0.1779 _p(c.sensitive) _{= Q} j g^ _p(c.insensitive) _{= Q} ^44

Rule 68

Gene 1: SID 512355 ESTs Highly similar to SRC SUBSTRATE P80/85 PROTEINS [Gallus gallus] [50AAO59424 30AAO57835]

Gene 2: SID W 346587 Homo sapiens quiescin (Q6) mRNA complete cds [5':W79188

30W74434]

Drag: CPT,10-OH

Parameters: μ_k ^sen = 0.8282, μι^sen = 1.001 μ_k ^insen = -0.1885, ^" = -0.2285 σ_k ^av = 0.9162, σι^avg = 0.8549, _Pk ^Vg = 0.18 _p(C.sensitive) ₌ Q ^^ _p(c.insensitive) _{= 0 8144}

Rule 69

Gene 1: ASNS Asparagine synthetase Chr.7 [510206 (IW) 50AAO53213 30AAO53461] Gene 2: SID W 346587 Homo sapiens quiescin (Q6) mRNA complete cds [50W79188 30W74434] Drag: CPT,10-OH Parameters: μ_k ^sen = -0.7243, μι^sen = 1.001 μ_k ^insen = 0.1648, μι^insen = -0.2285 σ_k ^avg = 0.9358, σι^av = 0.8549, _Pk/^Vg = -0.06293

P(Cι^sensitive) = 0.1856, p(C_i ^insensitive) = 0.8144

Rule 70 Gene 1: GAMMA-INTERFERON-INDUCIBLE PROTEIN IP-30 PRECURSOR Chr.19 [310021 (I) 5': 3':N99151]

Gene 2: Human extracellular protein (Sl-5) mRNA complete cds Chr.2 [485875 (EW) 5':AA040442 30AAO4O443] Drug: CPT,10-OH Parameters: μ_k ^sen = -0.9086, μfⁿ = 0.7657 μ_k ^insen = 0.2078, μι^insen = -0.1743 σ_k ^avg = 0.8915, σι^avg = 0.9202, _Pk ^Vg = -0.1283 _p(c.sensitive) _{= QA %56}} p_(C.insensitive) _{= QM44}

Rule 71

Gene 1: GAMMA-INTERFERON-INDUCIBLE PROTEIN IP-30 PRECURSOR Chr.19

[310021 (I) 5': 3':N99151]

Gene 2: Homo sapiens lysyl hydroxylase isoform 2 (PLOD2) mRNA complete cds Chr.3 [310449 (IW) 5':W30982 30N98463]

Drag: CPT,10-OH

Parameters: μ_k ^sen = -0.9086, μι^sen = 0.6335 μ_k ^insen = 0.2078, μι^insen = -0.1445 σ_k ^avg = 0.8915, σι^avg = 0.9558, _Pk ^Vg = 0.1739 _p(c.sensitive) _{= Q} j g^ _p(c. insensitive) _{= QM} Rule 72

Gene 1: GAMMA-INTERFERON-INDUCIBLE PROTEIN IP-30 PRECURSOR Chr.19 [310021 (I) 5': 30N99151]

Gene 2: SID W 486110 Profilin 2 [50AAO43167 30AAO4O7O3] Drug: CPT,10-OH Parameters: μ_k ^sen = -0.9086, μι^sen = 0.7038 μ_k ^insen = 0.2078, μι^insen = -0.1605 σ_k ^avg = 0.8915, σ,^avg = 0.9573, _Pk ^Vg = -0.08051 P( ^sensitive) = 0.1856, p( ^insensitive) = 0.8144

Rule 73

Gene 1: GAMMA-INTERFERON-INDUCIBLE PROTEIN IP-30 PRECURSOR Chr.19 [310021 (I) 5': 3':N99151] Gene 2: SID 42787 ESTs [50R59827 30R59717] Drug: CPT,10-OH Parameters: μ_k ^sen = -0.9086, μ_! ^sen = 0.5759 μ_k ^insen = 0.2078, _μι ^insen = -0.1318 σ_k ^avg = 0.8915, σj^avg = 0.961, _Pk/^V = 0.06258 _p(c.sensitive) _{= Q χ} g^ _p(c. insensitive) _{= QM 4}

Rule 74

Gene 2: SID 50243 ESTs [50H17681 3':H17066]

Drag: CPT, 10-OH

Parameters: μ_k ^sen = -0.9086, μι^sen = 0.8677 μ_k ^insen = 0.2078, μι^insen = -0.1977 σ_k ^avg = 0.8915, σj^avg = 0.9058, _Pk)1 ^avg = -0.1472 p_(c.sensitive) _{= 0> 1 856( p} ^.insensitive) _{= g l44} Rule 75

Gene 1: SID W 346587 Homo sapiens quiescin (Q6) mRNA complete cds [5':W79188 30W74434] Gene 2: SID 359504 ESTs [5': 30AAO1O589] Drug: CPT,10-OH Parameters: μ_k ^sen = 1.001, μfⁿ = -0.336 _μkinsen ₌ __{0 2285j μ]}insen _{= Q 07633} σ_k ^av = 0.8549, σι^av = 0.9733, _Pk ^Vg = 0.3387 p _C.sensitive) ₌ Q_{Λ S56>} p(_C. insensitive) _{= Q} g₁₄₄

Rule 76

Gene 1: SID 39144 ESTs Weakly similar to Rep-8 [H.sapiens] [50R51769 30R5177O] Gene 2: SID W 358526 ESTs [50W96O39 30W94821] Drug: CPT,20-ester (S) Parameters: μ_k ^sen = -0.8367, μι^sen = -0.771 μ_k ^insen = 0.2555, μj*⁵ = 0.2359 σ_k ^av = 0.8798, σι^avg = 0.9049, _Pk ^Vg = -0.2237 _p(c.sensitive) _{= Q 2344;} p₍q insensitive) _{= Q J656}

Rule 77

Gene 1: SID 39144 ESTs Weakly similar^'to Rep-8 [H.sapiens] [50R51769 3':R51770]

Gene 2: SID W 509633 ESTs Moderately similar to Kryn [M.musculus] [50AAO4556O

30AAO45561]

Drag: CPT,20-ester (S)

Parameters: μ_k ^sen = -0.8367, μι^sen = -0.8637 μ_k ^insen = 0.2555, μ = 0.2643 σ_k ^avg = 0.8798, _σι ^avg = 0.8771, _Pk ^Vg = -0.2147 p_(C.sensitive) ₌ 0 234^ p^.insensitive) _{= Q 656} Rule 78

Gene 1: SID 39144 ESTs Weakly similar to Rep-8 [H.sapiens] [50R51769 30R5177O] Gene 2: *Hs.648 Cut (Drosophila)-like 1 (CCAAT displacement protein) SID W 26677 ESTs [50R13994 30R39117] Drug: CPT,20-ester (S) Parameters: μ_k ^sen = -0.8367, μ,^sen = -0.652 μ_k ^insen = 0.2555, μι^insen = 0.1999 σ_k ^avg = 0.8798, σι^avg = 0.9431, _Pk ^Vg = -0.3363 _p(c.sensitive) ₌ ^34^ ^.insensitive) _{= Q} ^g

Rule 79

Gene 2: SID W 346510 Homo sapiens hCPE-R mRNA for CPE-receptor complete cds

[50W79O89 30W74492]

Drag: CPT

Parameters: μ_k ^sen = 0.4583, μι^sen = -0.4683 μ_k ^insen = -0.161, μι^insen = 0.1634 σ_k ^avg = 0.9838, σι^av = 0.9573, _Pk ^Vg = 0.6575 _p(c.sensitive) _{= 0 25}g₄^ _P(_C.insensitive) _{= Q 406}

Rule 80

Gene 1: ESTs Chr.19 [485804 (EW) 50AAO4O35O 30AAO4O351] Gene 2: Glyoxalase-I-log Drag: CPT,20-ester (S) Parameters: μ_k ^sen = -0.7177, μι^sen = -0.5058 μ_k ^insen = 0.2573, μι^insen = 0.1814 σ_k ^avg = 0.8936, σι^avg = 0.9632, _Pk ^Vg = -0.3337 P( ^sensitive) = 0.2644, P(Q^insensitive) = 0.7356

Rule 81

Gene 1 : Human G/T mismatch-specific thymine DNA glycosylase mRNA complete cds Chr.X [321997 (IW) 50W37234 30W37817]

Gene 2: SID W 358526 ESTs [5':W96039 30W94821]

Drug: CPT,ll-formyl (RS)

Parameters: μ_k ^sen = 0.626, μι^sen = -1.055 μ_k ^insen = -0.151, ^" = 0.2536 σ_k ^avg = 0.977, σι^av = 0.8569, _Pk ^V = 0.3776 _p(c.sensitive) _{= 0 l}g_39> p ^.insensitive) _{= Q} g_Q61

Rule 82 Gene 1: SID W 135118 GATA-binding protein 3 [50R31441 30R31442] Gene 2: SID W 358526 ESTs [50W96O39 30W94821] Drug: CPT, 11 -formyl (RS) Parameters: μ_k ^sen = 0.9817, μfⁿ = -1.055 μ_k ^insen = -0.2359, μι^insen = 0.2536 σ_k ^avg = 0.9021, σ ^vg = 0.8569, _Pk/^Vg = 0.08481 _p(c.sensitive) ₌ 0^939^ _p(c.insensitive) _{= Q gm}

Rule 83 Gene 1: ESTs Chr.16 [154654 (RW) 50R55184 30R55185]

Gene 2: SOD2 Superoxide dismutase 2 mitochondrial Chr.6 [144758 (EW) 50R76245 30R76527]

Drag: CPT,11-formyl (RS) Parameters: μ_k ^sen = 0.874, μι^sen = -0.7046 μ_k ^insen = -0.2102, μι^insen = 0.1693 σ_k ^av = 0.9112, σι^avg = 0.9543, _Pk ^Vg = 0.3184 _p(c.sensitive) ₌ Q -^3^ p₍Q insensitive) _{= Q} g^

Rule 84

Gene 1: SID W 358526 ESTs [50W96O39 30W94821] Gene 2 : Glutathione S-Tranferase A 1 -log Drag: CPT, 11 -formyl (RS) Parameters: μ_k ^sen = -1.055, μι^sen = -0.6283 μ_k ^insen = 0.2536, μι^insen = 0.1488 σ_k ^avg = 0.8569, σι^avg = 0.9702, _Pk ^Vg = -0.125 _p(c.sensitive) _{= Q m9> p} ^.insensitive) _{= Q g}

Rule 85

Gene 1: SID W 358526 ESTs [50W96O39 30W94821] Gene 2: PIGF Phosphatidylinositol glycan class F Chr.2 [486751 (IEW) 50AAO428O3 30AAO44616] Drag: CPT, 11 -formyl (RS) Parameters: μ_k ^sen = -1.055, μι^sett = -0.4069 μ_k ^insen = 0.2536, μι^insen = 0.09808 σ_k ^avg = 0.8569, σι^av = 1.003, _Pk ^Vg = -0.3618 p_(c.sensitiye) _{= Q} -^ p_(c.insensitive) _{= Q} gQgj

Rule 86 Gene 1 : PROTEASOME COMPONENT CI 3 PRECURSOR Chr.6 [344774 (IW)

5':W74742 3':W74705]

Gene 2: SID W 484681 Homo sapiens ES/130 mRNA complete cds [5':AA037568

30AAO37487]

Drag: Mechlorethamine Parameters: μ_k ^sen = 0.6562, μι^sen = -0.8883 _μkinsen 19

σ_k ^avg = 0.9627, σι^av = 0.9254, _Pk ^V = 0.5304

^.sensitive) ₌ Q ₁₉2_8j p(_C. insensitive) _{= QM72}

Rule 87 Gene 1: SID 43609 ESTs [5':H06454 30HO6184]

Gene 2: SID W 53251 Human Zn-15 related zinc finger protein (rlf) mRNA complete cds [5':R15988 3':R15987] Drag: Mechlorethamine Parameters: μ_k ^sen = 1.042, μι^sen = -0.5622 _μkinsen ₌ _Q₂₄g_3> ^insen ₌ ^345 σ_k ^av = 0.8728, σι^avg = 0.9712, _Pk ^Vg = 0.3407 _p(c.sensitive) _{= Q 1928j p(C}.insensitive) _{= Q η2}

Rule 88

Gene 1: CDH2 Cadherin 2 N-cadherin (neuronal) Chr. [325182 (DIRW) 50W48793 30W49619]

Gene 2: Homo sapiens (clone 35.3) DRAL mRNA complete cds Chr.2 [324636 (IW) 50W46933 30W46835] Drag: Geldanamycin Parameters: μ_k ^sen = -0.8842, μι^sen = 0.09839 μ_k ^insen = 0.225, μι^insen = -0.02426 σ_k ^av = 0.8839, σι^av = l, _Pk ^Vg = 0.6697 p(_C.sensitive) _{= 0 2}033, ^⁵*³¹⁸^⁶) = 0.7967

Rule 89

Gene 1: ESTsSID 327435 [50W32467 30W1983O] Gene 2: ESTs Chr.3 [377430 (IW) 50AAO55159 30AAO55O43] Drug: Morpholino-adriamycin Parameters: μ_k ^sen = 0.7559, μι^sen = 1.064 _μk ^insen ₌ _₀.1₅₀8, _μi ^insen = -0.212 σ_k ^av = 0.9646, σι^avg = 0.9006, _Pk ^Vg = -0.2502 _p(c.sensitive)

Quadratic Discriminant Analysis - 2-dimensional (QDA 2D)

This method computes a Bayesian conditional probability P^<j Cr"^ve\gk^J,gi) that a cell line f is sensitive to drug ^z , given the abundances of genes k and 1,

^k and ' , respectively, in cell line ^ .

The probability is computed using the following equation:

iGi ^itive(g gj)-p(cr^mve)

P(jeCr"^ve\gk^J,gj) = G sensitive _ „ \ τ>( /.sensitives . -.insensitive / „ i 's r> /-.insensitive s , ft,; (g gι)- (^Ci )⁺i^GkΛ (gi,gι)-P(C, )

where

P(Cr^,sitive) = prior probability ofthe sensitive set I (.sensitive I (.sensitive i . I ₍.insensitive

prior probability ofthe insensitive

bivariate gaussian density fitted to the histogram of gene k and / abundances. over the sensitive cell lines when subjected to drag i .

where

μ_k ⁿ = mean of gene k abundances over the sensitive cell lines

* = standard deviation of gene k abundances in the sensitive cell lines MΓ = mean of gene 1 abundances over the sensitive cell lines

' = standard deviation of gene 1 abundances in the sensitive cell lines

P^k,ι ₌ correlation coefficient of gene k and gene / abundances in the sensitive cell lines

. ^fflute'vβ(_<g , 5- ) = joint probability of abundance values gjanά gj from the bivariate gaussian density fitted to the histogram of gene k and / abundances over the insensitive cell lines when subjected to drug i .

where μ^{in en} _ _{mean 0}f g_ene k abundances over the insensitive cell lines insen ^k = standard deviation of gene k abundances in the insensitive cell lines

. insen ^l = mean of gene 1 abundances over the insensitive cell lines

—.insert

' = standard deviation of gene 1 abundances in the insensitive cell lines insen

P^k,ι — correlation coefficient of gene k and gene abundances in the insensitive cell lines

Sample parameters for the QDA 2D analsis ofthe NCI60 dataset are:

Rule l

Gene 1: BMIl Murine leukemia viral (bmi-1) oncogene homolog Chr.lO [418004 (REW) 5':W90704 3*:W90705] Gene 2: Human small GTP binding protein Rab7 mRNA complete cds Chr.3 [486233 (IW) 5*:AA043679 3':AA043680] Drag: Baker's-soluble-antifoliate Parameters: μ_k ^sen = 0.2314, μ,^sen = 0.3177, σ_k ^sen = 1.437, of¹ = 1.51, _Pk,ι^sen = -0.06216 μ_k ^insen = - 0.07175, _μι ^insen = -0.0982, ^" = 0.7941, σι^insen = 0.7097, p_kj^nscn = - 0.3688 _p(c.sens^itive) _{= 0>2}361, p(C_i ^insensitive) = 0.7639

Rule 2

Gene 1: IL8 Interleukin 8 Chr.4 [328692 (DW) 5':W40283 3':W45324] Gene 2: X-ray induction of CIPl/WAFl-log Drag: Cyanomorpholinodoxorabicin Parameters: μ_k ^sen = 0.856, μι^sen = 0.6131, σ_k ^sen = 0.6623, σι^sen = 0.9005, _Pk)ι^sen= 0.4391 μ_k ^insen = -0.224, μι^iπsen = -0.1602, σk^insβ, = 0.9401, _σι ^insen = 0.9451, p_kJ^osm = - 0.5299

P(Cι^sensitive) = 0.2067, p( ^insensitive) = 0.7933

Rule 3

Gene 1 : SID W 45954 H.sapiens mRNA for testican [5':H08669 3':H08670]

Gene 2: SID W 359443 Human ORF mRNA complete cds [5':AA010705 3":AA010706]

Drag: Cyanomorpholinodoxorabicin

Parameters: μ_k ^sen = 0.8178, μι^sen = 0.7159, σ_k ^sen = 0.9544, σι^sen = 0.6062, _Pk,ι^sen = -0.8806 μ_k ^insen = -0.2139, μι^insen = -0.1865, 0^ = 0.8419, σι^iπsen = 0.9949, p_κr^ea =

0.3109 _p(C.s^ens^itiv^e) ₌ Q ₂067,- p(Q^insensitive) = 0.7933

Rule 4

Gene 1 : SID W 242844 ESTs Moderately similar to ! ! ! ! ALU SUBFAMILY J WARNING ENTRY !!!! [H.sapiens] [5':H94138 3':H94064] Gene 2: ESTs Chr.l [488132 (IW) 5':AA047420 3':AA047421] Drug: Mitozolamide

Parameters: μ_k ^sen = -1.008, μι^sen = 0.4755, σ_k ^sen = 0.5668, σf²⁵ = 0.3355, _Pk,ι^sen= 0.3703 μ_k ^insen = 0.2536, μι^insen = -0.1193, α^⁶¹¹ = 0.9027, σT* = 1.066, _Pk, ^en = - 0.2131 _p(c.sensitive) ₌ Q _2Q06> p₍Q insensitive) _{= Q ?994}

Rule 5

Gene 1 : SID W 242844 ESTs Moderately similar to ! ! ! ! ALU SUBFAMILY J WARNING ENTRY !! !! [H.sapiens] [5*:H94138 3':H94064]

Gene 2: ZFP36 Zinc finger protein homologous to Zfp-36 in mouse Chr.19 [486668

(DIW) 5':AA043477 3':AA043478]

Drag: Mitozolamide Parameters: μ_k ^sen = -0.3906, μι^sen = -1.008, σ_k ^sen = 0.5337, σι^sen = 0.5668, _Pk,ι^sen = 0.1073 μ_k ^insen = 0.09821, μι^insen = 0.2536, σ_k ^insen = 1.044, σι^{insen ■}= 0.9027, p_ϋ ^in8βα = -

0.3729 _p(c.sensitive) ₌ Q ₂₀06, p(C ^Sensitive) = 0.7994

R Ruullee 66

Gene 1: SID W 242844 ESTs Moderately similar to !!!! ALU SUBFAMILY J WARNING ENTRY !! !! [H.sapiens] [5':H94138 3':H94064] Gene 2: SID W 323824 NADH-CYTOCHROME B5 REDUCTASE [5':W46211 3':W46212]

Drag: Mitozolamide Parameters: μ_k ^sen = -1.008,. μι^sen = 0.2421, σ_k ^sen = 0.5668, σfⁿ = 0.4385, _Pk,ι^sen = 0.04634 μ_k ^insen = 0.2536, μι^insen = -0.06095, σk^to8H1 = 0.9027, σj^insen = 1.078, p_kJ^B∞a = - 0.1944

P(Q^sensitive) = 0.2006, p(Q^insensitive) = 0.7994 Rule 7

Gene 1: ESTs Chr.6 [146640 (I) 5':R80056 3*:R79962] Gene 2: SID W 242844 ESTs Moderately similar to ! ! ! ! ALU SUBFAMILY J WARNING ENTRY ! ! ! ! [H.sapiens] [5':H94138 3*:H94064] Drag: Mitozolamide Parameters: μ_k ^sen = -0.3763, _μι ^sen = -1.008, σ_k ^sen = 0.5482, σf¹¹ = 0.5668, _Pk ^en= -0.7153 μ_k ^insen = 0.09352, _μι ^iπsen = 0.2536, σ_k ^insen = 1.034, _σr^en = 0.9027, p_kJ^BSm = - 0.1007 _P(_C.sensitive) _{= Q 20}06, p(C;^inSensitive) = 0.7994

Rule 8

Gene 1: SID 276915 ESTs [5':N48564 3':N39452] Gene 2: SID 301144 ESTs [5*:W16630 3':N78729] Drug: Mitozolamide Parameters: μ_k ^sen = 0.001165, μι^sen = 0.7785, σ_k ^sen = 0.4, σι^sen = 0.2994, _Pk ^en = -0.3594 μ_k ^insen = -0.0009506, _μι ^insen = -0.1951, a_k ^tosen = 1.068, σr^en = 1.014, p_κr^ea = 0.2265 _P(_C.sensitive) _{= 0 2}006, p(C ^sensitive) = 0.7994

Rule 9

Gene 1: Homo sapiens HuUAPl mRNA for UDP-N-acetylglucosamine pyrophosphorylase complete cds Chr.l [486035 (DIW) 5':AA043109 3':AA040861] Gene 2: SID W 242844 ESTs Moderately similar to ! ! ! ! ALU SUBFAMILY J

WARNING ENTRY !!!! [H.sapiens] [5':H94138 3':H94064]

Drag: Mitozolamide

Parameters: μ_k ^sen = 0.3574, μι^sen = -1.008, σ_k ^sen = 0.5869, σ_! ^Sen = 0.5668, _Pk;1 ^sen= 0.3711 μ ^insen = -0.09028, μι^insen = 0.2536, α^⁶¹^ 1.028, _σι ^insen = 0.9027, _k,ι^insen = -

0.1971 _p(c.sensitive) ₌ Q_2Q06J p_(C. insensitive) _{= Q}_₇₉₉₄ Rule 10

Gene 1 : SID W 510182 H.sapiens mRNA for kinase A anchor protein [5':AA053156 3':AA053135]

Gene 2: SID W 242844 ESTs Moderately similar to ! ! ! ! ALU SUBFAMILY J WARNING ENTRY ! ! ! ! [H.sapiens] [5':H94138 3':H94064] Drag: Mitozolamide Parameters: μ_k ^∞a = -0.4282, μι^sen = -1.008, σ_k ^sen = 0.4124, o ™ = 0.5668, _k,,^sen= 0.1487 μ_k ^insen = 0.1064, μι^insen = 0.2536, σ_k ^insen = 1.07, σι^insen = 0.9027, p_kjj⁰"* ** 0.03962

P(Q^sensitive) = 0.2006, p(c_i ^insensitive) = 0.7994

Rule 11

Gene 1 : SID W 242844 ESTs Moderately similar to ! ! ! ! ALU SUBFAMILY J WARNING ENTRY !!!! [H.sapiens] [5':H94138 3':H94064] Gene 2: SID 488362 ESTs [5':AA046764 3':AA046492]

Drug: Mitozolamide Parameters: μ_k ^sen = -1.008, μι^sen = 0.5996, σ_k ^sen = 0.5668, σι^sen = 0.3048, _k,,^seιl = -0.238 μ_k ^insen = 0.2536, _μι ^iπsen = -0.1504, ^" = 0.9027, σι^insen = 1.035, p ^ea =

0.1442 _p(c.sensitive) _{= 0>2}()06, p(Q^tosensitive) = 0.7994

Rule 12 Gene 1 : SID W 242844 ESTs Moderately similar to ! ! ! ! ALU SUBFAMILY J

WARNING ENTRY !!!! [H.sapiens] [5':H94138 3':H94064]

Gene 2: ESTs Highly similar to HYPOTHETICAL 13.6 KD PROTEIN IN NUP170-

ILS1 INTERGENIC REGION [Saccharo Chr.12 [415646 (IW) 5':W78722 3':W80529]

Drag: Mitozolamide Parameters: μ_k ^sen = -1.008, μι^sen = 0.4566, σ_k ^sen = 0.5668, σι^sen = 0.413, _Pk ^en = 0.02745 μ_k ^insen = 0.2536, μι^insen = -0.1139, σk^tasβl = 0.9027, σι^insen -= 1.038, Pk,ι^insaι =

0.3175 ^.sensitive) ₌ Q₂QQ6_t p_(C.insensitive) _{= QJ9Q l}

Rule 13

Gene 1: ESTs Weakly similar to R06B9.b [C.elegans] Chr.l [365488 (IW) 5':AA009557 3':AA009558]

Gene 2: SID W 380674 ESTs [5*:AA053720 3':AA053711]

Drug: Mitozolamide

Parameters: μ_k ^sen = 0.5214, _μι ^sen = 1.093, σ_k ^sen = 0.4503, σι^sen = 1.032, _Pk,ι^sen = 0.2533 μ_k ^insen = -0.1312, μι^insen = -0.2739, σk^insen = 1.016, σι^insen = 0.7614, fv^iD801 = -

0.2896

P(Q^sensitive) = 0.2006, p(Q^h∞liώivβ) = 0.1994

Rule 14 Gene 1: ESTs Chr.l [366242 (I) 5': 3':AA025593]

Gene 2: SID W 242844 ESTs Moderately similar to 11 J! ALU SUBFAMILY J

WARNING ENTRY !!!! [H.sapiens] [5':H94138 3':H94064]

Drag: Mitozolamide

Parameters: μ_k ^sen = -0.2007, μι^sen = -1.008, σ_k ^sen = 0.4757, σι^sen = 0.5668, p»ki^sen = -0.2512 μ_k ^insen = 0.04952, μ "⁸⁶¹¹ = 0.2536, σ_k ^insen = 1.076, σι^{insen •}= 0.9027, p_k ^am = -

0.1109 _p(c.sensitive) _{= 02}O06, p_(c.insensitive) _{= 0} gg₄

Rule 15

Gene 1 : Human mRNA for reticulocalbin complete cds Chr.l 1 [485209 (IW) 5':AA039292 3*:AA039334] Gene 2: SID 147338 ESTs [5': 3':H01302] Drug: Cyclodisone Parameters: μ_k ^sen = 0.6598, μfⁿ = 0.1958, σ_k ^sen = 0.2562, σf¹¹ = 0.3673, _Pk,ι^sefl = -0.6593 μ_k ^insen = -0.1341, μ ^en = -0.04021, σk^insen = 1.038, 0^ = 1.061, _Pk)r^en = 0.2816 _p(c.sensitive) _{= 0 689>} p^.insensitive) _{= Q g3} j j

Rule 16

Gene 1: SID W 51940 BETA-2-MICROGLOBULIN PRECURSOR [5':H24236 3':H24237]

Gene 2: SID W 486110 Profilin 2 [5':AA043167 3':AA040703]

Drag: Cyclodisone

Parameters: μ_k ^sen = 0.6766, μι^sen = 0.615, σ_k ^sen = 0.5551, σι^sen = 0.4072, _Pk ^en = 0.9224 μ_k ^insen = -0.1373, μr^en = -0.1252, σ_tT^sn = 0.996, _σι ^insen = 1.031, pιj^aa = -

0.313 _p(c.sensitive) = Q.1689, p(C_i ^inSensitive) = 0.831 1

Rule 1 17/ Gene 1 : Human DNA sequence from clone 1409 on chromosome Xpl 1.1 -11.4. C Coonnttaaiinnss aa IInntteerr--AAllpphhaa--TTrryyppssiinn IInnhh CChhrr..XX [[448855119944 ((II)) 55**::AAAA003399441166 33''::AAAA00339i 316]

Gene 22:: HHuummaann mmRRNNAA ffoorr rreettiiccuullooccaallbbiinn ccoommpplleettee ccddss CChhrr..ll 11 T [448855220099 ( (IIWW))

5':AA039292 3':AA039334]

Drug: Cyclodisone Parameters: μ_k ^sen = 0.2487, μι^sen = 0.6598, σ_k ^sen = 0.4569, σι^sen = 0.2562, _Pki^sen = -0.4186 μ_k ^insen = -0.05158, μι^insen = -0.1341, σk^in8ffll = 1.039, σι^insen = 1.038, p_kJ^mΛ =

0.2219 _p(C.sensitive) _{= 0# 1689>} p₍ insensitive) _{= 0} g_{31 1}

Rule 18

Gene 2: SID W 345624 Human homeobox protein (PHOXl) mRNA 3' end [5':W76402 3':W72050]

Drag: Clomesone Parameters: μk _c ^sen = 0.8248, μι^sen = -0.253, σ_k ^sen = 0.7407, σfⁿ = 0.7545, _Pk,ι^sen = 0.793 μ_k ^inMn = -0.1956, μι^5nsen = 0.06021, α^⁶¹¹ = 0.9082, σ_l ^iaam = 1.037, py^insen = 0.7103 _p(c.sensitive) _{= Q} j^ _p(qinsensitive) _{= g()g3}

Rule 19

Gene 1: MSN Moesin Chr.X [486864 (IW) 5':AA043008 3*:AA042882] Gene 2: Human mRNA for reticulocalbin complete cds Chr.l 1 [485209 ( 5 5''::AAAA003399229922 33''::AAAA003399333344]1

Drag: Clomesone Parameters: μ_k ^sen = 0.6791, μfⁿ = 0.4913, σ_k ^sen = 0.4486, _σι ^sen = 0.4435, _Pk,ι^sen = 0.8962 μ_k ^insen = -0.1612, _μι ^insen = -0.1165, σ^ = 1.026, σι^insen = 1.058, py^in8en =

0.04721 _p(c.sensitive) ₌ Q ^^ _p(c.insensitive) _{= 0>g()g3}

Rule 20

Gene 1: SID W 36809 Homo sapiens neural cell adhesion molecule (CALL) mRNA complete cds [5':R34648 3':R49177]

Gene 2: SID W 487535 Human mRNA for KIAA0080 gene partial cds [5':AA043528 3":AA043529]

Drag: Clomesone

Parameters: μ_k ^sen = 0.6335, μι^sen = 1.184, σ ^sen = 0.7063, σ,^sen = 0.9042, _Pk,,^sen = 0.2103 μ_k ^insen = -0.1498, μι^insen = -0.2817, 0^ = 0.9826, σι^{i en} = 0.7835, p_k,ι^insen = - 0.3389 _p(c.sensitive) _{= Q> 1917j p}(_C.insensitive) _{= Q m}

Rule 21

Gene 1: SID W 471748 ESTs [5':AA035018 3':AA035486] Gene 2: SID 147338 ESTs [5': 3':H01302] Drug: Clomesone Parameters: μ_k ^sen = 1.066, μι^sen = 0.1604, σ_k ^sen = 0.9178, σι^sen = 0.37, _Pk,ι^sen = -0.3953 μ_k ^insen = - 0.2526, μ^ = -0.03847, 0^ = 0.7849, σι^to8βn = 1.074, p_kJ^oaaL = 0.494

P(Ci^sensitive) = 0.1917, p(c_i ^insensitive) = 0.8083

Rule 22

Gene 1: ESTs Chr.X [48536 (E) 5':H14669 3':H14579] Gene 2: SID W 242844 ESTs Moderately similar to !!!! ALU SUBFAMILY J WARNING ENTRY !!!! [H.sapiens] [5':H94138 3':H94064] Drag: Clomesone Parameters: μ_k ^sen = -0.8957, μf^en = -1.079, σ_k ^sen = 0.7433, σι^sen = 0.7048, _Pk ^en = -0.6495 μ_k ^tasβn = 0.2117, μι^insen = 0.2564, σ_k ^insen = 0.8949, σι^insen = 0.8653, py^insβ, = - 0.08726

^.sensitive) _{= 0}.191_7j p(Q insensitive) ₌ Q gQgg

Rule 23

Gene 1 : SID W 487535 Human mRNA for KIAA0080 gene partial cds [5':AA043528

3':AA043529]

Gene 2: SID W 488333 ESTs [5':AA046755 3':AA046642] Drug: Clomesone

Parameters: μ_k ^sen = 1.184, μι^sen = -0.1604, σ_k ^sen = 0.9042, σι^sen = 0.8711, _Pk,ι^sen = -0.1011 μ_k ^insen = -0.2817, μι^insen = 0.03825, σk^insβ'¹ = 0.7835, oi^in8e,l = 1.011, p_kj^Dam =

0.4544 _p(C.sensitive) = 0.1917, p( ^fasensitivβ) = 0.8083

Rule 24

Gene 1: ESTs Chr.8 [470141 (IW) 5':AA029870 3':AA029318] Gene 2: SID W 487535 Human mRNA for KIAA0080 gene partial cds [5':AA043528 3':AA043529] Drag: Clomesone Parameters: μ_k ^sen = 0.4978, μι^sen = 1.184, σ_k ^sen = 0.4895, σι^sen = 0.9042, _Pk ^en = 0.6156 μ_k ^insen = -0.1176, μr^en = -0.2817, σk^iπ8β,1 = 1.056, α^⁶^ 0.7835, _Pk,r^en = 0.1011

^.sensitive) ₌ Q 19^ _p(C.insensitive) _{= Q} ^3

Rule 25

Gene 1: BINDING REGULATORY FACTOR Chr.l [485933 (IW) 5':AA040819 3':AA040156]

Gene 2: SID 43555 MALATE OXIDOREDUCTASE [5':H13370 3':H06037] Drug: Fluorouracil (5FU) Parameters: μ_k ^sen = 0.5584, μfⁿ = 0.9686, σ_k ^sen = 1.073, afⁿ = 0.4053, _k ^en = -0.839 μ_k ^insen = -0.1082, μι^insen = -0.1883, σk^in8en = 0.9367, σι^insen = 0.9657, p_kjl ^insβ,, = - 0.3566 _p(-_c.sensitive) _{= Q 1628j} p^.insensitive) _{= Q>8372}

R iiirrrvvvuuuuliiieeee 26ooo

Gene 1 : ESTsSID 327435 [5':W32467 3*:W19830] Gene 2: SID 289361 ESTs [5':N99589 3':N92652] Drug: Fluorouracil (5FU) Parameters: μ_k ^sen = 0.9982, μι^sen = 0.03614, σ_k ^sen = 1.157, σfⁿ = 0.186, _Pk,_! ^Sen = -0.4795 _μk ⁱn^sen ₌ _ _{l 943j} ^ⁱn^sen ₌ __Q Q07432, ₀^ = 0.8258, _G^ = 1.074, pyT*

0.09915 _p(-_c.sensitive) _{= 0#1628j p(C}. insensitive) _{= QMn}

Rule 2 L7I

Gene 1: ESTsSID 327435 [5':W32467 3':W19830]

G ^■ ecniiec 22:: HH..ssaappiieennss mRNA for Gal-beta(l-3/l-4)GlcNAc alpha-2.3-sialyltransferase Chr.l l [324181 (IW) 5':W47425 3':W47395] Drug: Fluorouracil (5FU) Parameters: μ_k ^sen = 0.9982, _μι ^sen = -0.3532, σ_k ^sen = 1.157, σf^en = 0.2383, _Pk,ι^sen = 0.01963 μ_k ^insen = -0.1943, μr^en = 0.06805, σ_k ^tosen = 0.8258, of = 1.049, p_kJ^aam = 0.2537 _p(c.sensi^tive) ₌ Q.1628, P(Q^inSensitive) = 0.8372

Rule 28 Gene 1 : SID W 116819 Homo sapiens clone 23887 mRNA sequence [5':T93821 3':T93776]

Gene 2: ELONGATION FACTOR TU MITOCHONDRIAL PRECURSOR Chr.16 [429540 (IW) 5':AA011453 3':AA011397] Drag: Fluorodopan Parameters: μ_k ^sen = 0.4215, μι^sen = -0.3324, σ_k ^sen = 1.115, σ-^sen = 1.519, _Pk ^en = 0.5573 μ_k ^insen = -0.1101, μι^insen = 0.0863, σk^insβl = 0.9491, σι^insen =.0.7573, p_y ^insen = -

0.786 _p(c.sensitive) ₌ Q.2061 , p(C_i ^insensitive) = 0.7939

Rule 29

Gene 1: ESTs Chr.14 [244047 (I) 5':N45439 3':N38807]

Gene 2: SID 307717 Homo sapiens KIAA0430 mRNA complete cds [5': 3':N92942]

Drag: Cyclocytidine Parameters: μ_k ^sen = 0.536, μι^sen = 0.004825, σ_k ^sen = 0.4307, αf* = 0.232, _Pk,ι^sen = 0.1655 μ_k ^insen = -0.1816, μι^insen = -0.002083, 0^ = 1.03, σι^Sn∞l = 1.151, p r ⁿ =

0.08986 _p(c.sensitive) ₌ Q_{25 > p(c}.insensitive) _{= Q 467}

Rule 3 300

Gene ! 1: SID W 510230 Homo sapiens (clone CC6) NADH-ubiquinone oxidoreductase subun oiitt i mRNA 3' end cds [5*:AA053568 3*:AA053557]

Gene ! 22:: SSIIDD 307717 Homo sapiens KIAA0430 mRNA complete cds [5': 3*:N92942] Drag: Cyclocytidine

Parameters: μ_k ^sen = 0.1566, _μι ^sen = 0.004825, σ_k ^sen = 0.4745, σι^sen = 0.232, p_k,ι^sen = -0.4326 μ_k ^insen = -0.05336, μι^insen = -0.002083, σk^insβn = 1.116, or⁸*¹ = 1.151, PtJ⁰"" ** 0.3113 _p(C.sensitive) _{= Q 7467}

Rule 31 Gene 1: DNA POLYMERASE EPSILON CATALYTIC SUBUNIT A Chr.12 [321207 (IW) 50W5291O 3':AA037353]

Gene 2: SID 307717 Homo sapiens KIAA0430 mRNA complete cds [5': 3':N92942] Drag: Cyclocytidine Parameters: μ_k ^sen = 0.7918, μι^sen = 0.004825, σ_k ^sen = 1.042, σι^sen = 0.232, _Pk ^en = 0.176 μ_k ^insen = -0.2694, μι^iπsen = -0.002083, σk^insen = 0.762, σι^Jaββn = 1.151, _Pk,ι *^ea = - 0.06434 _p(c.sensitive) ₌ 0^533^ _p(C.insensitive) _{= 0>7467}

Rule 32

Gene 1: TXNRDl Thioredoxin reductase Chr.12 [510377 (IW) 5':AA055407 3':AA055408]

Gene 2: ESTs Chr.l [362126 (I) 5':AA001086 3':AA001049] Drug: Mitomycin Parameters: μ_k ^sen = 0.9736, μp* = -0.4653, σ_k ^sen = 0.752, σ?^en = 0.3908, _k ^en = 0.1693 μ_k ^insen = - 0.2247, μι^insen = 0.107, α^⁶" = 0.8952, σι^insen = 1.053, pι ^insβn = 0.3972 _p(c.sensitive) _{= 0# 1872j} p^.insensitive) _{= Q} g^g

Rule 33

Gene 1: SID W 260223 Human mRNA for BST-1 complete cds [5':N45417 3':N32106] Gene 2: TXNRDl Thioredoxin reductase Chr.12 [510377 (IW) 5':AA055407 3':AA055408] Drug: Mitomycin Parameters: μ_k ^sen = 0.1887, μι^sen = 0.9736, σ_k ^sen = 0.6724, σι^sen = 0.752, _Pki^sen = 0.7526 μ_k ^insen = -0.04347, μι^insen = -0.2247, σ^⁶^ 1.003, σι^insen = 0.8952, p_kJ^oam = - 0.007584 _p(c.sensitive)

Rule 34 Gene 1 : SCYA2 Small inducible cytokine A2 (monocyte chemotactic protein 1 homologous to mouse Sig-je) Chr.l7 [108837 (DIW) 5':T77816 3':T77817] Gene 2: *Carbonic anhydrase II SID 429288 [5':AA007456 3':AA007360] Drag: Anthrapyrazole-derivative Parameters: μ_k ^sen = 0.8903, μι^sen = -0.3723, σ_k ^sen = 0.9679, σι^sen = 0.694, _Pk,,^sen = -0.4114 μ_k ^insen = -0.224, μι^insen = 0.09341, σ_k ^insen = 0.8509, σι^insen = 1.03, p_k ^am = 0.4247

P(Cι^sensitive) = 0.2006, p(c_i ^insensitive) = 0.7994

Rule 35

Gene 1 : SID 356851 Homo sapiens mRNA for nucleolar protein hNop56 [5': 3':W86238]

Gene 2: Human extracellular protein (Sl-5) mRNA complete cds Chr.2 [485875 (EW) 5':AA040442 3':AA040443] Drag: Anthrapyrazole-derivative Parameters: μ_k ^sen = -0.216, μι^sen = 1.016, σ_k ^sen = 0.6331, σι^sen = 1.089, _Pk)ι^sen = -0.6461 μ_k ^insen = 0.05396, μι^insen = -0.2548, σk^insβl = l, σι^insen = 0.7749, p_w ^in8βl = 0.2101 _p(c.sensitive) ₌ Q.2006, p(C_i ^insensitive) = 0.7994

Rule 36

Gene 1: ALDHIO Aldehyde dehydrogenase 10 (fatty aldehyde dehydrogenase) Chr.17 [208950 (EW) 5':H63829 3':H63779]

Gene 2: SID W 488148 H.sapiens mRNA for 3'UTR of unknown protein [5':AA057239 3':AA058703]

Drag: Anthrapyrazole-derivative Parameters: μ_k ^sen = 0.6212, μfⁿ = 0.843, σ_k ^sen = 0.6852, of" = 0.575, _Pk ^en = 0.2169 μ_k ^insen = -0.1554, μf⁸⁶¹^ -0.2115, G_k ^ea = 0.9606, σT"¹ = 0.9263, Pkα^nsβI = - 0.3119 p_(c.sensitive) _{= Q 20}06, p_(C. insensitive) _{= Q η}gg

Rule 37

Gene 1: Human extracellular protein (Sl-5) mRNA complete cds Chr.2 [485875 (EW)

5':AA040442 3':AA040443]

Gene 2: SID W 415693 Homo sapiens mRNA for phosphatidylinositol 4-kinase. complete cds [5':W78879 3':W84724] Drag: Anthrapyrazole-derivative

Parameters: μ_k ^sen = 1.016, μι^sen = 0.3712, σ_k ^sen = 1.089, σι^sen = 0.4463, _Pk ^en = -0.3426 μ_k ^insen = -0.2548, μ^⁶¹¹ = -0.09229, α^⁶" = 0.7749, σι^insen = 1.066, p_kj^msn =

0.341 positive) _{= Q 20}06, p_(C.insensitive) _{= Q} ^4

Rule 38

Gene 1 : SID W 345683 ESTs Highly similar to INTEGRAL MEMBRANE

GLYCOPROTEIN GP210 PRECURSOR [Rattus norvegicus] [5':W76432 3':W72039] Gene 2: Human mRNA for KIAA0143 gene partial cds Chr.8 [488462 (IW)

5':AA047508 3*:AA047451]

Drag: Daunorabicin

Parameters: μ_k ^sen = 0.918, μfⁿ = -0.6559, σ_k ^sen = 0.3704, σf¹ = 0.4622, _Pk,ι^sen = -0.5746 μ_k ^insen = -0.2022, μι^insen = 0.1457, 0^ = 0.9271, σι^insen = 1.007, pk,ι^insβn = -

0.009774 _p(C.sensitive) ₌ Q.181 1, P( ^insensitive) = 0.8189

Rule 39 Gene 1: SID W 162077 ESTs [5':H25689 3':H26271] Gene 2: SID W 197549 ESTs [5':R87793 3':R87731] Drag: Deoxydoxorabicin Parameters: μ_k ^sen = -0:2102, μfⁿ = -0.1107, σ_k ^sen = 0.3133, _σι ^sen = 0.9712, _Pk ^en = -0.98 μ_k ^insen = 0.03539, μι^insen = 0.01824, σ_k ^insen = 1.068, σι^insen = 1.008, p r^&a = 0.1725

P( ^sensitive) = 0.1428, p(c_i ^insensitive) = 0.8572

Rule 40

Gene 1: ELONGATION FACTOR TU MITOCHONDRIAL PRECURSOR Chr.1

[429540 (IW) 5':AA011453 3':AA011397]

Gene 2: ESTs Chr.2 [365120 (IW) 5':AA025204 3':AA025124] Drug: Amsacrine

Parameters: μ_k ^sβn = -0.7939, μfⁿ = 0.558, σ_k ^sen = 1.022, σ?^sn = 1.102, _Pk ^en = 0.7045 μ_k ^insen = 0.2239, μι^insen = -0.1576, σk^in8βn = 0.791, _σι ^insen = 0.8965, p_kj^in8βl =

0.4064 p(_C.sensitive) ₌ ^ p_(Q insensitive) _{= 0} g

Rule 41

Gene 1: G6PD Glucose-6-phosphate dehydrogenase Chr.X [430251 (IW) 5':AA010317 3':AA010382] Gene 2: SID W 376708 ESTs [5':AA046358 3':AA046274] Drag: CPT,20-ester (S) Parameters: μ_k ^sen = -0.09704, μι^sen = -0.6823, σ_k ^sen = 0.4911, σfⁿ = 0.8524, _k)fⁿ = 0.7542 μ_k ^iMen = 0.02995, μι^insen = 0.2092, σ_k ^insen = 1.068, σι^insen = 0.9393, p _,ι^insβtt = - 0.5785

P(Q^sensitive) = 0.2344, p(c ^sensitive) = 0.7656

Rule 42

Gene 1: H.sapiens mRNA for ESM-1 protein Chr.5 [324122 (RW) 5':W46667 3':W46577]

Gene 2: Human FEZ2 mRNA partial cds Chr.2 [488055 (IW) 5':AA058551

3':AA053303]

Drag: CPT Parameters: μ_k ^sen = -0.1032, μι^sen = 0.8185, σ_k ^sett = 0.4146, σfⁿ = 0.8985, _Pk ^en = -0.6229 μ_k ^insen = 0.03592, μ,.^insen = -0.2863, a_k ^insm = 1.124, σι^insen = 0.8401, pk4^in8en =

0.4189 p(_C.sensitive) ₌ 0 2594, p_(C.insensitive) _{= 0 J4Q6}

Rule 43

Gene 1: SID W 361023 ESTs [5*:AA013072 3':AA012983] Gene 2: H.sapiens mRNA for TRAMP protein Chr.8 [149355 (IEW) 5':H01598 30HO1495] Drag: CPT Parameters: μ_k ^sen = -0.6506, μι^sen = 0.5667, σ_k ^sen = 0.6739, σι^sen = 1.274, _Pk ^en = 0.7093 μ_k ^insen = 0.2279, μι^insen = -0.1978, 0^ = 0.9778, σ,^insen = 0.7508, p_kfι^insen = - 0.1771

^.sensitive) ₌ Q.2594, p(_C.insensitive) _{= 0 4Q6}

Rule 44

Gene 1: SID W 358754 Human mRNA for cysteine protease complete cds [5':W94449 3':W94332]

Gene 2: SID W 159512 Integrin alpha 6 [5':H16046 3':H15934]

Drug: CPT

Parameters: μ_k ^sen = -0.1082, μι^sen = 0.7291, σ_k ^sen = 0.7356, σ_! ^Sen = 0.6557, _Pk,ι^sen = -0.6645 μ_k ^insen = 0.0372, μι^insen = -0.2559, σk^insen = 1.038, σι^insen = 0.9638, frj^oam =

0.4712 _p(c.sensitive) _{= 0>25}94_{5 p(C}.insensitive) _{= Q 4Q6}

Rule 45 Gene 1 : SID 257009 ESTs [5':N39759 3':N26801]

Gene 2: SID W 488148 H.sapiens mRNA for 3'UTR of unknown protein [5':AA057239

3':AA058703]

Drag: CPT Parameters: μ_k ^sen = 0.3448, μι^sen = 0.8224, σ_k ^sen = 0.7661, σι^sen = 0.5588, _Pk,ι^sen = 0.6149 μ_k ^insen = -0.1208, μ ^ea = -0.2881, σk^ta8βn = 1.029, σι^insen = 0.9329, p_k, *^a =

0.06046 p(Qsensitive) _{= 0 25}g _{i p(c}.insensitive) _{= 0 4Q6}

Rule 46

Gene 1: SID 43609 ESTs [5':H06454 3*:H06184]

Gene 2: SID W 361023 ESTs [5':AA013072 3':AA012983] Drug: CPT,20-ester (S)

Parameters: μ_k ^sen = 0.4667, μι^sen = -0.6333, σ_k ^sen = 1.301, σfⁿ = 0.554, _Pk ^en = 0.5266 μ_k ^insen = - 0.1602, μι^insen = 0.2168, 0^ = 0.7751, σ,^insen = 0.9858, nJ^om =

0.2268 _P(C.sensitive) ₌ Q^ _p(c.insensitive) ₌ Q ^

Rule 47

Gene 1 : Human G/T mismatch-specific thymine DNA glycosylase mRNA complete cds Chr.X [321997 (IW) 5':W37234 3':W37817] Gene 2: SID W 358526 ESTs [5':W96039 3':W94821] Drag: CPT, 11 -formyl (RS) Parameters: μ_k ^sen = 0.626, μι^sen = -1.055, σ_k ^sen = 1.041, σι^sen = 1.241, _k ^en = -0.1072 μ_k ^insen = -0.151, μι^insen = 0.2536, σ_k ^insen = 0.9295, _σι ^insen = 0.7034, p_k,ι^in8βl = 0.6208 _p(c.sensitive) _{= 0} ^939^ p( insensitive) _{= 0>gQ61}

Rule 48

Gene 1 : PROTEASOME COMPONENT CI 3 PRECURSOR Chr.6 [344774 (IW) 5':W74742 3':W74705]

Gene 2: SID W 484681 Homo sapiens ES/130 mRNA complete cds [5':AA037568

3':AA037487]

Drug: Mechlorethamine Parameters: μ_k ^sen = 0.6562, μfⁿ = -0.8883, σ_k ^sen = 0.7248, σι^sen = 0.7952, _Pk ^en = -0.1383 μ_k ^insen = -0.1565, μι^insen = 0.2119, σ_k™^ea = 0.9825, _σι ^insen = 0.9257, p_k,r^ea =

0.6324 p(_C.sensitive) _{= Q} j ^ _p(c.insensitive) _{= QMn}

Rule 49

Gene 1: AKl Adenylate kinase 1 Chr.9 [488381 (IW) 5':AA046783 3':AA046653] Gene 2: Human vascular endothelial growth factor related protein VRP mRNA complete cds Chr.4 [309535 (I) 5': 3':N94399] Drag: Mechlorethamine Parameters: μ_k ^sen = -0.4881, μι^sen = -0.243, σ_k ^sen = 1.786, σfⁿ = 0.4893, _Pk ^en = 0.8105 μ_k ^insen = 0.1157, _μι ^insen = 0.05762, σ^ = 0.6286, σι^insen = 1.08, p_kj^in8βl, = 0.03238 _p(c.se^nsitive) ₌ Q.1928, p(c_i ^{inseιlsitive}) = 0.8072

Rule 50

Gene 1: SID W 489301 ESTs [5':AA054471 3':AA058511] Gene 2: Human epithelial membrane protein (CL-20) mRNA complete cds Chr.12

[488719 (IW) 5':AA046077 3':AA046025]

Drag: Melphalan

Parameters: μ_k ^sen = 0.9792, μι^sen = -0.619, σ_k ^sen = 1.075, σι^sen = 0.7439, _Pk,ι^sen = -0.8227 μ ^insen = -0.2399, μι^insen = 0.1515, α^⁶¹¹ = 0.7994, _σι ^insen = 0.9531, p_k, ^m =

0.3178

P(Q^sensitive) = 0.1967, p(C_i ^insensitive) = 0.8033

Rule 51 Gene 1 : SID W 245450 Human transcription factor NFATx mRNA complete cds [5':N77274 3':N55066]

Gene 2: SID W 485645 KERATIN TYPE II CYTOSKELETAL 7 [5':AA039817 3':AA041344] Drag: 5-Hydroxypicolinaldehyde-thiose

Parameters: μ_k ^sen = 0.122, μι^sen = 0.8712, σ_k ^sen = 0.2463, σι^sen = 0.6735, _k ^en = 0.1308 μ_k ^iMβn = -0.02658, μι^insen = -0.1896, 0^ = 1.091, _σι ^insen = 0.9271, p_y ^in8βn = 0.05545 _p(c.sensitive) _{= Q} -^ _p(c. insensitive) _{= Q} g_{21 1}

Rule 52

Gene 1: SID 381780 ESTs [5':AA059257 3':AA059223] Gene 2: SID 512355 ESTs Highly similar to SRC SUBSTRATE P80/85 PROTEINS

[Gallus gallus] [5':AA059424 3':AA057835]

Drug: Paclitaxel — Taxol

Parameters: μ_k ^sen = 0.1618, μι^sen = -0.8354, σ_k ^seri = 0.1828, σf" = 0.4935, _Pk,fⁿ = -0.09957 μ_k ^insen = -0.03218, _μι ^insen = 0.162, σ_k ^insen = 1.06, σι^insen = 0.9902, py^insm = -

0.09191

P(Cι^sensitive) = 0.1622, p(C_i ^insensitive) = 0.8378

Rule 53 Gene 1: SID 381780 ESTs [5':AA059257 3*:AA059223]

Gene 2: SID 130482 ESTs [5':R21876 3':R21877]

Drag: Paclitaxel — Taxol

Parameters: μ_k ^sen = 0.1618, μι^sen = -0.9271, σ_k ^sen = 0.1828, σι^sen = 0.3413, Pki^sen = -0.3935 μ_k ^insen = -0.03218, _μι ^insen = 0.1791, σ_k ^insen = 1.06, σι^insen = 0.9842, p_k ^in8βl = -

0.2741 _p(c.sensitive) _{= 0# 1622>} p( .-^ensitive) _{= α837g}

Rule 54 Gene 1: SID 344786 Human mRNA for KIAA0177 gene partial cds [5': 3':W74713] Gene 2: TXNRDl Thioredoxin reductase Chr.12 [510377 (IW) 5*:AA055407 3':AA055408] Drag: Bisantrene Parameters: μ_k ^sen = -0.3189, μι^seα = 1.298, σ_k ^sen = 0.6532, σι^sen = 0.7515, _Pk ^en = 0.9897 μ_k ^iDsea = 0.02732, _μr^&n = -0.1115, σ_k ^insm = 0.9915, σi^⁶¹¹ = 0.9088, p_k,r^eΛ =

0.06623 p(_C.sensitive) ₌ Q _07889> p(Q insensitive) ^ _Q g₂ j _χ

Determining Statistical Significance of Finding

Mean Square Error (MSE) scores are calculated by comparing the probabilities (a form of likelihood) computed by a method against an ensemble of surrogate data generated by different randomizations, i.e., permutations, ofthe original data

(creating artificial samples). A resulting histogram of MSE scores is then interpreted as representing the probability distribution of error; hence, the statistical significance of any given determined probability can.be assigned. The gene expression levels can then be selected according to the ranking of their probability for the original data, with a comparison against the MSE score for the randomized data.

Validating Predictions of Sensitivity to Drug, for each Method

For any given gene k and drug 7, a cross-validation procedure is used to assess validity of any prediction. For example, we omit 1 given cell line from consideration, and carry out a given method on the remaining cell lines, and record the findings. The omitted cell line is restored and a different cell line is omitted, and the given method re-applied. This is repeated, one cell line at a time, until all the cell lines have had their turn being omitted. All the findings are compiled. Difference scores between an original calculation and a cell line- omitted calculation are obtained. Mean Square Errors (MSE) are then calculated from the aggregated differences. MSE is then an assessment ofthe validity of the given method.

Sample results from one ofthe Bayesian classifiers (the LDA 2D) on the NCI60 dataset are shown in Table 8 below. Table 8

The above steps as performed on, by way of example, the NCI60 dataset can be further explained as follows.

Start off with 2 tables of data: a table, T, with gene expression data and a table, A, with drag concentration data. In table T each column is a gene, each row is a cell line and each entry is the expression level of a gene in a given cell line.

In table A, each column is a drug, each row is a cell line (corresponding exactly to the same cell lines in table T) and each entry is the drug concentration which inhibits the growth of a given cell line by 50%.

Note: The same cell lines appear in Tables T and A, and the order ofthe cell lines is the same in both tables. In the NCI60 analysis there were 60 cell lines, 1000 genes and 90 drags.

Table T

Table A

An example of Tables T and A with actual data are shown below:

Table T: Gene expression values

Table A: -logGI50 values

1) Transform the drug response values.

Form a new table which corresponds to the A table by transforming the numerical values of Table A so that they fall on a continuous numerical scale > 0 and < 1. This is done in order to represent the intensity ofthe attribute in a readily interpretable manner: 0 represents negligible insensity (e.g., insensitive to drag) and 1 represents high intensity (e.g., sensitive to drug), with continuous gradation in between.

For example, using equation for the continuous piece- wise linear biological scoring function described previously:

Let ay represent the entry in the ith row and jth column of table A.

Transform each entry, ay, as follows: if ay is less than 0.3 then set ay = 0 if ay is between 0.3 and 0.7, then set ay = (ay - 0.7) / 0.3 if ay is greater than or equal to 0.7, then set ay = 1

If a new entry ay is > 0 , consider cell line i to be at least partially sensitive to drug j. If a new entry ay is less < 1 , consider cell line i to be at least partially insensitive to drag j.

Based on the transformed attribute values in some column j, it is possible to separate cell lines into 2 classes, C^sensitive and c^in8ensitivβ. Cell lines that are sensitive are in class C^sensitive and cell lines that are insensitive are in the C^insensitive class. But, some cell lines can be considered to be partially in both class. For example, if the transformed value ay = x, then cell line i is considered to be x*100% in class C^sensitive and (l-x)*100% in class pinsensitive

2) Example Application of Bayesian Classifiers - UGDA ID, UGDA 2D, LDA ID, QDA ID, LDA 2D, QDA 2D.

Note: Steps explained using LDA ID are equivalently applied for any ofthe other Bayesian classifiers. Example of Steps :

Apply LDA ID to measure how well a given gene co-occurs, associates with, or predicts response to a given drug.

2.1) Select a column, T_k, from the T matrix, with the expression values of some gene k. Select a column, A_t-, from the A matrix, with the drug concentrations (e.g., in units of- log₁₀GI50) values of some drag i [see paragraph Id in the Methods document for GI50].

2.2) Remove the first entry, Ti_k, from column T_k and the first entry, A_J , from column A_t. Assume that these entries belong to cell line Z₇.

2.3) Separate the remaining entries, (I^ through T_n>k in column T_k into two sets:

- one set, _ic^sensιtιve has the gene expression values of cell lines at least partially sensitive to drag (i.e. these cell lines have values greater than 0 in column A,)

- a second set, _iC^nsemιt , has the gene expression values of cell lines at least partially insensitive to drag i (i.e. these cell lines have values smaller than 1 in column A_t )

2.4) Compute the weighted mean, μ_k ^sensitive , and the weighted standard deviation, σ_k "^sitive , ofthe values in set ₁C^ensitive. Find the weighted mean, μ™* ' , and the weighted standard deviation, _σk ^>nse"^{s ive} ofthe values in set iC^insensitive.

Find the weighted average standard deviation σ_k ^vg ofthe two sets.

Find the frequency, p^C⁶™' ), ofthe sensitive class and the frequency, ^.c"^teve), of the insensitive class. Compute parameters necessary to fit any chosen mathematical density ftmction or continuous curve to a α category-wise histogram ofthe type described previously. 2.5) Compute the probability, P^ e c^sensilive \ T_Uk), that cell line Lj is sensitive to drag , using the information ofthe expression level of gene k and the proportion, i.e., frequency, ofthe sensitive and insensitive classes. Namely, compute

.sensitive /r s /.sensitives

P(L e C^*ensitive I T_u) = i^k <J .T ^ΛY- ) .sensitive /r s p/ .sensitive , .insensitive /rp s . / (.insensitive s

where

as described previously.

2.6) Calculate an error for the probability derived in step 2.5.

Consider the probability from step 2.5 to be the expected probability, p^eχPeoted ₅ that cell line L is sensitive to drag . Consider entry An to be the observed probability, p^observed ₅ 5 that cell line Lj is sensitive to drug i.

Then, calculate an error, E_h based on these two values, where Ej = (P^>αPected - p^e^™*)\

2.7) A cross-validation procedure.

For each cell line, find the probability of sensitivity to drug i.

Restore the first entries of columns T_k and Ai, (entries belonging to cell line Li) 0 and remove the second entry of these columns. Assume that the removed entries belong to cell line C₂. Repeat steps 2.3 through 2.6, to obtain the probability of cell line E₂ being sensitive to drag . Follow the same procedure for each ofthe cell lines. Find the mean ofthe error terms, E, from all the iterations. This value is referred to as the mean squared error (MSE). This MSE quantifies how well 5 gene k predicts sensitivity to drug i.

3) Find the MSE scores of all genes versus all drugs.

4) A statistical significance assessment procedure. Find initial significance p-values for all MSE scores.

A significance p-value indicates the likelihood that an MSE score could have arisen by chance (i.e. that randomized data (i.e., the original data, randomly permuted to obliterate any patterns that may have been in the original data) could have generated the MSE score).

4.1) Construct a distribution, i.e., histogram, of MSE scores from the LDA ID being applied to randomized data.

In each column ofthe I⁷ table, randomly rearrange the order ofthe entries. In each column of the A table, randomly rearrange the order ofthe entries. Make copies of these two tables, and again randomly rearrange the entries in all columns. Repeat this procedure until there are 100 randomized versions ofthe 2 tables. Apply steps 2 and 3 to each ofthe randomized pairs of tables. In other words, for each pair of tables, find the MSE scores of all genes versus all drugs. This results in a total of 100,000 MSE scores (1000 scores for a single pair of tables * 100 pairs of tables). Such scores are referred to as MSE^rand. MSE scores from non-randomized tables are referred to as MSE^nonrand

4.2) Compare MSE scores from non-randomized data tables to MSE from randomized data tables.

For a given MSE score, Mi, from non-randomized tables, determine the fraction of MSE^rand scores which are lower than Mj. This fraction is the significance p-value for score Mj. Using this approach, determine the significance p-values for all MSE^nonrand scores.

5) Adjust the significance p-values associated with MSE^nomand scores to correct for multiple tests significance test being employed.

The initial significance p-values associated with MSE^nonrand scores may not necessarily fairly reflect the trae statistical signficance because there were multiple significance tests employed. Thus, multiply each significance p-value by 1000 to take into account that 1000 genes were tested against each drug. This kind of adjustment of statistical significance to account for multiple significance tests being employed is known in the statistical literature as the Bonferroni method. 6) Report by cell line and drug, the genes and the probabilities derived in step 2.5 6.1) Particularly identify in the report those cell lines and drugs for which there are genes for which the probability derived in step 2.5 is high, say >0.85, and ranked by smallest-to-largest significance p-score.

The examples set out above provide general principles that may be extended to other fields of study, and are not intended to limit the scope ofthe invention. For example, drag sensitivity levels reflecting the inhibiting of growth could be replaced by drag sensitivity that reflects toxic reactions to drags. This could be useful in finding markers that indicate circumstances where a given drag not only does not help, but may cause harm (be toxic to non-diseased cells). Diagnostic kits can then be derived to search for those markers in given patients.

Similarly, examples of characterizing attributes could be SNPs or proteins (proteomics).

The Bayesian classifiers are not limited to 1 dimensional or 2 dimensional classifiers, rather any dimension of classifier could be used as appropriate for the chosen characterizing attribute set. This may or may not turn up additional significant likelihoods of co-occurrences depending on the relationships of the attributes in the dataset. It is recognized that a brute force approach of carrying out all steps for all combinations of characterizing attributes and attributes sets of interests can require a great deal of time and computational power, particularly with higher order combinations of attributes. Pre-processing techniques, such as those mentioned previously, can be employed to reduce the number of candidate characterizing attribute sets, and thus the amount of time and computational power required.

Alternate methods could be used to create artificial samples in place ofthe randomizations suggested herein. The randomizations used herein proved to be a simple and effective manner of creating the artificial samples. In the examples provided above, two likelihood thresholds have been used. First, a likelihood threshold based upon the artifical samples. Second, a likelihood threshold based upon the assigned likelihoods being above a certain percentile of all assigned likelihoods for the relevant attribute of interest.

The likelihood threshold can also be based on a selected threshold based on empirical knowledge, statistically derivation, or otherwise. In order to capture all characterizing sets of interest, even those that could possibly lack statistical validity, the likelihood threshold could simply be set at zero. Expanding on this, the likelihood threshold could be a selected numerical threshold, or the threshold could be varied, to determine the effect on the results. The likelihood threshold need not be based on artificial or random data in order to derive useful results from the methods. As we have seen, the likelihood thresholds could be a single threshold, or a combination of likelihoods thresholds.

The methods described herein can be embodied in a computer program running on an appropriate computing platform as shown in Fig. 9. The combination ofthe computing platform and computer program results in a system for determining co-occurrences of characterizing attributes and attribute sets of interest. Again, the examples shown in the Figures are not intended to be limiting to the breadth ofthe invention. As will be evident to those skilled in the art, other configurations of computing platforms and computer programs are possible. For example, the computing platform could take the form of computer network with the computer program distributed about the network, or accessed by terminals remote from that part of the computing platform running the computer program. For example, the computer program may be running on a computer that is connected to and accessible through the Internet.

An example flow diagram for the preferred embodiment of software embodying the first base method described above is shown in Fig. 9. Similarly, an example general block diagram for an embodiment of a system for determining co-occurrences of characterizing attributes and attributes of interest is shown in Fig. 10. In this example, a computer program 1001 is stored on computer storage media 1003 (such as a hard disk from which the computer program is loaded into memory ofthe computer at the time the program is run) of a standalone computer 1005. The dataset is stored in a database 1007 accessible to the computer 1005. The ranked characterizing attribute sets resulting from the base methods may be reported and stored in a file on the hard disk 1003 for later use, including as an output display for viewing on a computer monitor 1009 ofthe computer 1005. They may take an alternative form of output display as a report 1011 generated on a printer 1013. Similarly, they may be reported to a file, or other output display across a computer network 1015.

Flow diagrams for embodiments of a number of other base methods are shown in Figs. 11, 13 and 15. Corresponding block diagrams are shown in Figs. 12, 14 and 16. The methods, system and other aspects ofthe embodiments described herein, and the invention, can be used to identify markers for diagnosis, such as might form part of diagnostic kits or procedures used to determine a disease or syndrome type of a patient. Similarly, they may be used to identify markers for prognosis of a disease or syndrome of a patient, such as might form part of diagnostic kits or procedures used to determine a disease or syndrome type of a patient. Similarly, they may be used to identify markers to determine whether a therapy or treatment is appropriate for a patient, or other biological attribute of a human or other living system. This can be done by identifying and attribute set to be tested for in the patient or other living systemby carrying out one or more of the base methods previously described. Although the methods, system and other aspects ofthe embodiments have been described primarily with respect to the use of gene level expression sets as attribute sets, the embodiments and the invention may also be applied to tissue or serum protein concentration sets, or blood or tissue molecular marker sets, or microscopic or macroscopic clinical observables, or combinations thereof.

It will be understood by those skilled in the art that this description is made with reference to the preferred embodiment and that it is possible to make other embodiments employing the principles ofthe invention which fall within its spirit and scope as defined by the following claims.

Claims

We claim:

1. A method of identifying one or more characterizing attributes for an object that are likely to co-occur with one or more attributes of interest for the object, the method comprising the steps of:

Selecting one or more attribute sets of one or more characterizing attributes of the object,

Selecting an attribute set of one or more attributes of interest for the object,

Assigning a likelihood for each characterized attribute set that the attribute set occurs for the object when the attribute set of interest occurs for the object, each likelihood determined using one or more Bayesian computable classifiers on a dataset of attributes for a plurality of actual samples ofthe object,

Comparing each assigned likelihood against one or more likelihood thresholds, and Reporting the assigned likelihoods of the characterizing attribute set based on the likelihood thresholds.

2. The method of claim 1 or 7, wherein a likelihood threshold for each characterizing attribute set is determined using the same Bayesian classifiers as the assigned likelihood on a dataset of attributes for a plurality of artificial samples ofthe object.

3. The method of claim 1 or 7, wherein a likelihood threshold for each characterizing attribute set is determined by computing those characterizing attribute sets with an assigned likelihood above a given percentile of all assigned likelihoods for the relevant attribute set.

4. The method of claim 2 or 24, wherein the artificial samples are created by randomizing the actual gene expression levels for the characterizing attributes.

5. The method of claim 2or 24, wherein the artificial samples are created by transposing the actual gene expression levels for each characterizing attribute to another characterizing attribute.

6. The method of claim 1 , wherein the assigned likelihoods of the remaining characterizing attribute sets are also compared against a second likelihood threshold determined by computing those characterizing attribute sets with an assigned likelihood above a given percentile of all assigned likelihoods for the relevant attribute set of interest.

7. A method of identifying a characterizing attribute for an object that is likely to co- occur with an attribute of interest for the object, the method comprising the steps of:

Selecting one characterizing attribute set of one or more attributes for the object,

Selecting an attribute of interest for the object, Assigning a likelihood for the characterized attribute set that the attribute occurs for the object when the attribute of interest occurs for the object, the assigned likelihood determined using a Bayesian computable classifier on a dataset of attributes for a plurality of actual samples ofthe object,

Comparing the assigned likelihood against a likelihood threshold, and Reporting the assigned likelihood of the characterizing attribute set based on the likelihood threshold.

8. The method of claim 7 or 24, wherein the characterizing attributes are gene expression levels and the attribute of interest is a drag sensitivity level.

9. The method of claim 1, wherein each characterizing attribute is a gene expression level and the attribute of interest is a drug sensitivity level.

10. The method of claim 1 , wherein each characterizing attribute is a gene expression level and the attribute of interest is drag dose (absolute concentration or dose relative to some standard dose) along an increasing, or decreasing, scale.

11. The method of claim 1, wherein each characterizing attribute is a gene expression level and the attribute of interest is the dose of drag which causes half-maximal cellular growth rate.

12. The method of claim 1, wherein each characterizing attribute is a gene expression level and the attribute of interest is - logarithm _ι0 (dose) , where dose is the dose which yields half-maximal total cell mass accumulating under otherwise standard conditions.

13. The method of claim 9, the drug sensitivity level represents growth inhibiting in diseased cells.

14. The method of claim 9, the drag sensitivity level represents a lack of growth inhibiting in diseased cells.

15. The method of claim 9, the drag sensitivity level represents patient toxicity in healthy cells.

16. The method of claim 9, wherein the attributes are represented in a dataset taken from the NCI60 dataset.

17. The method of claim 7 or 24, wherein the Bayesian classifier is selected from a group consisting of linear discriminant analysis, quadratic discriminant analysis, and a uniform/gaussian analysis.

18. The method of claim 1 , wherein the Bayesian classifiers are selected from a group consisting of linear discriminant analysis, quadratic discriminant analysis, and a uniform/gaussian analysis.

19. The method of claim 1, wherein two Bayesian classifiers are used selected from a group consisting of linear discriminant analysis, quadratic discriminant analysis, and a uniform/gaussian analysis.

20. The method of claim 1 , wherein one Bayesian classifier is used selected from a group consisting of linear discriminant analysis, quadratic discriminant analysis, and a uniform/gaussian analysis.

21. The method of claim 1, wherein the Bayesian classifiers are linear discriminant analysis, quadratic discriminant analysis, and a uniform/gaussian analysis.

22. The method of claim 1, wherein the characterizing attribute sets ranked following comparison ofthe likelihood and the likelihood threshold are reported.

23. The method of claim 22, wherein the ranked characterizing attributes sets are reported to one of a group consisting of a computer readable file stored on computer readable media, a printed report, and a computer network.

24. A method of identifying one or more characterizing attributes for an object that are likely to co-occur with one or more attributes of interest for the object, the method comprising the steps of: selecting one or more attribute sets of one or more characterizing attributes ofthe object, selecting an attribute set of one or more attributes of interest for the object, assigning a likelihood for each characterized attribute set that the attribute set occurs for the object when the attribute set of interest occurs for the object, each likelihood determined using one or more Bayesian computable classifiers on a dataset of attributes for a plurality of actual samples ofthe object, determining a likelihood significance for each assigned likelihood using artificial samples, and ranking the assigned likelihoods ofthe characterizing attribute set using the likelihood significance.

25. The method of claim 24, wherein the assigned likelihoods are ranked by assigned likelihood and subranked by likelihood significance.

26. The method of claim 24, further comprising the steps of: comparing the assigned likelihood against a likelihood threshold, and reporting the assigned likelihood ofthe characterizing attribute set based cm the likelihood threshold and the ranking ofthe assigned likelihood.

27. A method of identifying one or more characterizing attributes for an object that are likely to co-occur with one or more attributes of interest for the object using a dataset of samples of attributes for the object, the method comprising accessing one ofthe systems of claim 28.

28. A system for identifying one or more characterizing attributes for an object that are likely to co-occur with one or more attributes of interest for the object using a dataset of samples of attributes for the object, the system comprising: a computing platform, and a computer program on a computer readable medium for use on the computer platform in association with the dataset, the computer program comprising: instructions to identify a characterizing attribute for an object that is likely to co-occur with an attribute of interest for the object, by carrying out the steps ofthe method of claim 1, 7 or 24.

29. A computer program on a computer readable medium for use on a computer platform in association with a dataset, the computer program comprising: instructions to identify a characterizing attribute for an object that is likely to co- occur with an attribute of interest for the object, by carrying out the steps ofthe method of claim 1 , 7 or 24.

30. A method of drag discovery comprising the steps: identifying characterizing attribute sets for interaction by the drag, wherein the step of identifying comprises carrying out the steps ofthe method of claim 1, 7 or 24 for drug sensitive attributes of interest, and performing screens for drugs where growth in cells having desirably ranked characterizing attribute sets is drag sensitive.

31. A method of identifying markers for diagnostic kits used to determine if a treatment is appropriate for a patient, the method comprising the steps: identifying a gene expression level set to be tested for in the patient by carrying out the steps ofthe method of claim 1, 7 or 24.

32. A method of identifying markers for diagnosis isof a living system, the method comprising the steps: identifying an attribute set to be tested for in the living system by carrying out the steps ofthe method of claim 1, 7 or 24.

33. A method of identifying markers for prognosis of a living system, the method comprising the steps: identifying an attribute set to be tested for in the living system by carrying out the steps ofthe method of claim 1, 7 or 24.

34. A method of identifying markers for determining the appropriateness of a therapy or treatment of a living system, the method comprising the steps: identifying an attribute set to be tested for in the living system by carrying out the steps ofthe method of claim 1, 7 or 24.

35. The method of claim 32, wherein the diagnosis is with respect to a disease or syndrome type of a patient.

36. The method of claim 33, wherein the prognosis is with respect to a disease or syndrome type of a patient.

37. The method of claim 32, 33 or 34, wherein the attributes ofthe attribute set comprise protein concentrations.

38. The method of claim 37, wherein the protein concentrations comprise tissue protein concentrations.

39. The method of claim 37, wherein the protein concentrations comprise seram protein concentrations.

40. The method of claim 32, 33 or 34, wherein the attributes ofthe attribute set comprise molecular markers.

41. The method of claim 40, wherein the molecular markers comprise blood molecular markers.

42. The method of claim 40, wherein the molecular markers comprise tissue molecular markers.

43. The method of claim 32, 33 or 34, wherein the attributes ofthe attribute set comprise clinical observables.

44. The method of claim 43, wherein the clinical observables comprise microscopic clinical observables.

45. The method of claim 43, wherein the clinical observables comprise macroscopic clinical observables.

46. The method of claim 32, wherein the markers are for diagnostic kits used in the diagnosis.

47. The method of claim 32, wherein the markers are for diagnostic procedures used in the diagnosis.

48. The method of claim 33, wherein the markers are for prognostic kits used in the prognosis.

49. The method of claim 33, wherein the markers are for prognostic procedures used in the prognosis.